# From design to device: challenges and opportunities in computational
discovery of p-type transparent conductors

Rachel Woods-Robinson*1,2,3, Monica Morales-Masis4, Geoffroy Hautier5, Andrea
Crovetto6 1Clean Energy Institute, University of Washington, Seattle, WA,
98195 United States, 2Materials Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720 United States, 3Materials Science Center,
National Renewable Energy Laboratory, Golden, CO, 80401 United States, 4MESA+
Institute for Nanotechnology, University of Twente, 7500 AE Enschede, the
Netherlands, 5Thayer School of Engineering, Dartmouth College, 14 Engineering
Dr, Hanover, NH, USA, 6Centre for Nano Fabrication and Characterization (DTU
Nanolab), Technical University of Denmark, 2800 Kongens Lyngby, Denmark

(September 1, 2024)

###### Abstract

A high-performance p-type transparent conductor (TC) does not yet exist, but
could lead to advances in a wide range of optoelectronic applications and
enable new architectures for, e.g., next-generation photovoltaic (PV) devices.
High-throughput computational material screenings have been a promising
approach to filter databases and identify new p-type TC candidates, and some
of these predictions have been experimentally validated. However, most of
these predicted candidates do not have experimentally-achieved properties on
par with n-type TCs used in solar cells, and therefore have not yet been used
in commercial devices. Thus, there is still a significant divide between
transforming predictions into results that are actually achievable in the lab,
and an even greater lag in scaling predicted materials into functional
devices. In this perspective, we outline some of the major disconnects in this
materials discovery process — from scaling computational predictions into
synthesizable crystals and thin films in the laboratory, to scaling lab-grown
films into real-world solar devices — and share insights to inform future
strategies for TC discovery and design.

##  I Introduction

Many advances in renewable energy technology are limited by the quality and
availability of materials. Conversely, materials advances enable technological
advances. The search for p-type transparent conductors (TCs) exemplify this:
achieving a p-type TC with comparable properties to n-type transparent
conducting oxides (TCOs) could enable new solar cell architectures and
transparent electronics applications, serving as hole-selective top contacts
or electrodes, buffer layers, back contacts for bifacial solar cells, and
window layers in tandem devices.[1, 2] Many candidate p-type TCs have been
proposed and explored, such as delafossites Cu$M$O2[3] and CuI,[4] among
others. Yet the realization of high-performing p-type TCs is still a major
research challenge, and p-type TCs for photovoltaic (PV) applications remain
limited. This is in part due to fundamental physical trade-offs of combining
transparency, p-type doping, and high hole mobilities in wide band gap
oxides.[5]

While the first p-type TCs have been found by serendipity and following
chemical principles (e.g., Cu-based TCs), it is now possible to identify new
p-type TCs using first principles computations and high-throughput screening.
“High-throughput” computational screenings usually begin with selection of a
set of input compounds often from material databases (e.g., Materials
Project[6], AFLOW[7], OQMD[8], among others), followed by a series of
filtration steps using computed descriptors which represent proxies for
certain materials properties.[9]

As depicted in Figure 1(a), after selection of inputs a typical screening
method for p-type TCs starts with (1) a proxy for thermodynamic stability
(usually energy above convex hull, $E_{\mathrm{hull}}$), (2) a proxy for
absorption edge above the visible regime (usually the Kohn-Sham band gap,
$E_{\mathrm{G}}$), (3) a proxy for high hole mobility (usually hole effective
mass, $m_{\mathrm{h}}^{*}$), followed by a series of more computationally
demanding calculations for hole dopability, band alignment, synthesizability,
and other properties.[10, 11, 12, 13] As final screening steps as shown in
Figure 1(b), higher accuracy computational methods can be applied such as
hybrid or GW computations (to better estimate transparency) or electron-phonon
limited mobility (to estimate hole transport).

Computational screening research from the past decade has yielded promising
p-type TC candidates, and key discoveries are summarized in Figure 1. One of
the earliest studies focused on oxides and identified a handful new p-type TCO
candidates.[10] Since then, a wide array of follow-up research has refined the
screening criteria or input material set, for example by extending the
chemistries of interest beyond ternary oxides or by focusing on specific
chemistries or structures.[14, 15, 16, 17, 11, 18, 19, 20, 21, 22, 23, 24].
These studies have motivated the synthesis and characterization of a handful
of computationally identified candidates, with a few notable success stories.
For example, very high mobility and transparency have been observed
experimentally in moderately p-type Ba2BiTaO6[15, 25] and TaIrGe.[26]
Transparency and high p-type dopability has been confirmed experimentally in
Cr2MnO4, although with lower hole mobilities.[14] Overviews of experimentally
realized p-type TCs can be found in existing literature reviews and
benchmarking studies.[2] These successes, some of which are highlighted in
Figure 5(c), are encouraging and bring a new level of predictability for
computational materials science.

However, experimental confirmation of certain predictions has been either
incomplete or not up to the expectations of the prediction. Boron phosphide
(BP), for instance, has been identified as a promising p-type TC with previous
encouraging experimental results, but recent results indicate a difficulty to
reach expected performances, as described later in this paper.[11, 27, 28]
Ta2SnO6 showed promise yet could not be doped enough to measure hole
conductivity.[29] Other predicted materials remain to be grown and evaluated,
ideally in thin film form (e.g., K2Sn2O3, B6O, NaNbO2, BeSiP2).[10, 22, 30]
Progress in the field of predicted n-type TCs serves as an indicator that
advances in predicted p-type TCs are within reach; for example, as a success
of a high-throughput screening, a new computationally predicted n-type TCO
ZnSb2O6 has been recently confirmed by experiment with high doping and
mobility.[31, 32] However, despite the many successes, scaling up predicted
TCs for efficient optoelectronic device applications remains a major
challenge, and a variety of disconnects have emerged.

Historically, unwanted lab results and corresponding insights from researchers
are usually not published, leading to a literature bias and a difficulty
disentangling why disconnects are occurring. In this article, we identify and
contextualize these disconnects observed in the literature and within our own
research in scaling p-type TC predictions into PV devices. First, we design a
framework for the different stages involved in this process, as summarized in
Figure 3. Next, we highlight some of the most pernicious disconnects (see
Figure 4). One set of disconnects stem from transforming predicted candidates
into thin films in the lab: due to challenges including synthesizability,
phase purity, dopability, and unwanted absorption, so far no predicted p-type
TC grown in the lab performs on par with n-type TCOs. Another key set of
disconnect arises from transforming predictions into scalable optoelectronic
devices: challenges emerge such as making sufficient contact to the device,
tuning band alignments, and growing interfaces free of defects, and other
barriers. We also discuss specific barriers related to scalability and
sustainability. Lastly, we offer guidance on how the research community can
overcome such disconnects to bring our predictions into fruition in p-TCs and
beyond.

##  II Materials design-to-device framework for p-type TCs

###  II.1 PV applications of p-type TCs

While conventional silicon (Si) solar modules do not typically include contact
layers, emerging or “disruptive” PV such as silicon heterojunction (SHJ),
tunnel oxide passivated contact (TOPCon), thin film (e.g., CdTe and
perovskites), and tandem PV require various contact and buffer layer
configurations to transfer charge carriers throughout the device. The current
market share of PV (as of 2023) is mostly conventional Si, however various
projections show that meeting our net zero requirements by 2050 will likely
require rapid development and scaling of such emerging PV technology.[33]

Figure 1: (a) Example high-throughput screening method for p-type TCs.[12].
(b) Example of properties that are assessed during “deep dive” calculations.
(c) A literature review of p-type TC candidates predicted from high-throughput
computational studies, with candidates highlighted that are discussed in this
article. Pink shaded materials have been predicted first as p-type TCs by
computation, while grey shaded materials were first demonstrated
experimentally and are included here as references. “Most” likely p-type
dopable have been confirmed by experiment or have hybrid defect calculations,
while “least” likely have not been assessed for dopability. A full table with
references is reported in the SI.

One recent projection even expects “disruptive” PV deployment to ramp up
rapidly after 2030, and by around 2040 become dominant over conventional
Si.[34] However, as researchers develop these new configurations, few options
for inorganic p-type transparent contacts are available, which limits
architectures and performance. Therefore, developing appropriate contact
materials for emerging PV, especially p-type transparent contacts, is an
important research direction.

Figure 2: Schematic solar cell device architectures that incorporate p-type
TCs. The focus of materials in this paper is to enable architectures for
“Future PV applications,” including as (a) a top p-type electrode to a thin
film single junction stack, (b) a top or bottom electrode to a bifacial solar
cell, and (c) as a contact or window layer to a tandem solar cell. Panel (c)
depicts a 4 terminal mechanically-stacked tandem, in which a p-type semi-
transparent conductor (“semi-TC”) layer could serve as either the bottom
contact of absorber #1, as shown in (c), or the top contact of absorber #2 if
polarities are reversed. A p-type semi-TC could also be used in other tandem
configurations such as a window layer in a 2T monolithic tandem, however in
other cases lateral transport may be less important. Reported demonstrations
of p-type TCs in PV, in which lateral transport is also less important, are
included for reference: (d) semi-transparent bottom electrode or buffer for
thin film solar, and (e) hole transport layers (HTLs) in contact with an
n-type TC. Panel (f) summarizes properties that must be attained in each
configuration. This figure is intended to represent the key functionalities
that p-type TCs could enable, and is by no means exhaustive. Layers are
simplified for clarity, and thickness is not to scale.

Applications for p-type TCs span far beyond solar (e.g., flat panel displays,
transparent electronics, etc.), but for the purposes of this article we focus
mainly on PV devices. Within the field of emerging PV, p-type TCs with
different properties could serve a variety of roles. For example, p-type TCs
could have a double role, that of a hole carrier selective contact (where a
high work function is required) and that of a transparent electrode (where a
low sheet resistance is required, usually below 100 $\Omega$/sq). As a
selective contact, the p-type TC would extract photogenerated holes in the
absorber material, and, as transparent electrode, it would laterally transport
the carriers to the device terminal. Since the material is transparent, it
would also function as a window layer in the device. This would enable novel
device architectures for e.g., bifacial and semitransparent PV, which
currently relies exclusively on the use of n-type ITO. Representative device
designs are summarized in Figure 2, with future applications enabled by high-
performance p-type TCs highlighted in yellow in (a–c). Designing TCs with high
lateral (or “in-plane”) transport for these applications will be the focus of
this paper. For reference we also show two “out-of-plane” transport
configurations (d-e) in which mobility and dopability are less important,
buffer HTLs and semi-transparent contacts (in which transparency is less
important); since these applications have actually been demonstrated, they
will be used to showcase challenges scaling to devices in the “Lab-to-device
disconnects” section, but are not the focus of this paper. This variety of
applications is the origin of one important disconnect, which arises from an
intermingling of problem statements during the materials design stage. Namely,
a material predicted from an application-agnostic screening for p-type TCs may
not be appropriate to use in a specific application, e.g., might not provide
an adequate band alignment.

###  II.2 Materials discovery stages

To understand the disconnects that emerge when scaling up computationally-
predicted materials, we first summarize the main stages involved in going from
a predicted material, to a synthesized material, to the exploration of its
functionality on a device, and to a commercially-relevant device. For the
specific case of a p-type transparent conductor (p-type TC), the “design-to-
device” framework is summarized in Figure 3 with the following nine key
stages:

  1. 1.

Computational screening: As shown in Figure 1(a) a material is “screened” as a
promising candidate, often from a database. Simple computational metrics
representing properties of stability, transparency, and conductivity are
evaluated. Usually a stoichiometric, well-defined crystal structure is
assumed, though high-throughput defects have started to emerge and could be
used at this stage.[35, 36] Various biases can occur at this early stage that
introduce disconnects at later stages.

  2. 2.

Computational deep dive: Higher levels of theory (e.g., spin-orbit coupling,
GW) and more in-depth analysis methods are applied to more accurately predict
properties of synthesizability, transparency, temperature-dependent mobility,
carrier concentration from defects, and device-relevant parameters such as
band alignment, as shown in Figure 1(b). Structural models can be more complex
at this stage to account for off-stoichiometric and non-equilibrium effects.

  3. 3.

First experimental realization: The material is synthesized for the first
time, usually in the form of a bulk single crystal or powders (e.g., using
solid state synthesis or mechano-chemical synthesis). From powders or single
crystals it is possible to confirm crystal structure, stoichiometry, air and
moisture stability, and preliminary properties, though at this stage
optoelectronic properties are not usually assessed. When screening from a
database of known materials such as ICSD, this stage has often already been
realized, so occurs before stage 1.

  4. 4.

Thin film synthesis: The material is synthesized as a phase-pure, conformal
thin film. There are numerous methods to fabricate thin films, from wet
synthesis to chemical or physical vapor deposition, in one or multiple steps.
Thin film formation can result in epitaxial (single crystal), polycrystalline
or amorphous films. The microstructure, thickness, stoichiometry, point or
crystallographic defects, and surface effects will strongly influence the thin
film properties and functionality. Specific synthesizability challenges and
disconnects for p-type TC thin films have been discussed previously by
Fioretti and Morales-Masis. [37]

  5. 5.

Thin film optimization: A p-type TC film is optimized, with high transparency
and conductivity (the first reported film in stage 4 is usually not indicative
of how much performance can be tuned). Such optimization can occur through
doping, alloying, or optimizing process conditions such as annealing, among
other methods. Throughout this manuscript, “high” transparency refers to
$\alpha>10^{4}$ cm-1 and “high” conductivity refers to $\sigma>10$ S cm-1,
though what is considered sufficiently “high” depends on application. We note
this stage is often skipped for novel p-type TCs.

  6. 6.

Junction demonstration: A p-type TC is demonstrated to form a junction with
another material, either deposited directly on an active substrate or on top
of a device stack. The key challenges at this stage are chemical and thermal
compatibility (e.g., to avoid chemical intermixing at interfaces or material
degradation) and electronic alignment (e.g., valence band energies aligned),
as well as good physical adhesion, without degrading the junction partner
material.

  7. 7.

Solar cell demonstration: The p-type TC film is incorporated into a full solar
cell device stack with demonstrated performance. Often this first device has
low efficiency and poor device characteristics. p-type TCs can be deposited
directly on a substrate or on top of the device stack (depending on the device
polarity). The substrate or device stack will define the limitations for the
p-type TC film deposition, such as thermal budget. This in turn will influence
(or limit) the final properties of the p-type TC film.

  8. 8.

Solar cell optimization: The solar cell is optimized around the p-type TC
layer, with reasonable PV performance values such as open circuit voltage,
fill factor, and efficiency. At this stage, in addition to retaining good thin
film properties and optimizing the TC, the interfaces play a key role in
device performance. One way this disconnect can be alleviated by including
interface simulations once the device stack is known (e.g., as done for
organic and halide perovskite solar cells).[38]

  9. 9.

Scaling device to module level: Scaling up lab-scale devices (solar cells)
into commercially-relevant devices (solar modules) requires different
fabrication techniques for TCs, and can change properties and requirements. As
reported in NREL’s well-known PV efficiency charts,[39, 40] solar cell
properties often cannot be reproduced on the module scale, due to various
disconnects such as conformality and processing constraints. At this stage,
supply chain, cost, and sustainability factors also must all be considered.
The ultimate goal of materials discovery is to go from a predicted material to
this final stage.

This framework will be used throughout this manuscript to guide our
exploration of disconnects. These steps are inspired by the technology
readiness level (TRL) framework, which will also be referred to when
appropriate, however our steps 1–6 expand upon earlier stages of materials
design. This framework is designed and adopted specifically to address stages
of materials discovery in p-type TCs. We emphasize that discovery does not
necessarily follow this linear flow: often, optimization involves iteration
between these stages. Historically, most materials have been synthesized first
before computational predictions, and many recent studies return to
computational “deep dives” after experimental stages to fill in experimental
knowledge gaps or inform decisions. To highlight representative materials
discussed throughout this paper, Figure 5 illustrates the stage along the
“design-to-device” framework in which research and development is stalled, a
graphical highlight of research at this stage, and one of the key disconnects
describing why progress is stalled.

Figure 3: Our proposed framework of materials “design-to-device” progression
for p-type transparent conductors (TCs), representing stages of technological
innovation from computational prediction (far left) to a scalable device (far
right). Figure 1 highlights computational methods within the first two stages
of this process, while Figure 2 outlines various devices designs in stages
7–9.

##  III Design-to-lab disconnects

In this section, we outline key disconnects that emerge going from the
prediction of a new p-type TC (stage 1) to the realization of optimized
properties in an experimental p-type TC thin film (stage 5). Some common
disconnects at this stage are summarized in the left-hand side of Figure 4. We
will also discuss strategies to confront these challenges.

###  III.1 Computational screenings contain limitations and biases

First principles computations are powerful but have inherent fundamental
limits and numerical dangers such as convergence that need to be kept in mind.
We will assume in this discussion that computations have been performed by an
expert, achieving the necessary numerical convergence for the targeted task.
We will also assume that the inherent limits of the approximation used (e.g.,
semilocal DFT underestimates band gap and affects defect computations
dramatically) are taken into account in the screening process. Unfortunately,
a small fraction of studies in the field do not follow these requirements and
we warn the reader to always question whether computational results have been
interpreted with caution and the right amount of expertise.

The efficacy of high-throughput computational screenings emerges from the
quality of its input data, which usually comes from computational databases or
the ICSD. These repositories have grown substantially, encompassing diverse
materials and properties. However, extensively studied materials are over-
represented in these databases, which can induce a selection bias toward well-
studied candidates and can potentially neglect less-studied ones. For example,
previous literature suggests non-oxides as promising p-type TCs due to low
effective masses and low ionization potentials.[11] However, there are far
more oxides than non-oxides in materials databases, so non-oxides are under-
sampled in screening studies.

An algorithmic bias can lead to prioritizing properties that are more readily
calculated or accessible, potentially overlooking properties that may be more
representative of desired performance. In order to reduce the input materials
to a manageable subset, the initial steps of computational screenings usually
filter with property proxies that are computationally inexpensive (“cheap”) to
calculate. For example, although both effective mass $m^{*}$ and scattering
time $\tau$ contribute to achieving high mobility, given current algorithms
$m^{*}$ (“cheap” proxy) is much easier to calculate than $\tau$ (“expensive”
proxy). Additionally, hole conductivity depends on doping density which is
also difficult to compute. Therefore, we are biased towards looking for
materials with low $m^{*}$ rather than those with excellent scattering or
doping properties, and we need to be careful about how close a cheap proxy
correlates to an expensive proxy.

Similarly, a knowledge bias occurs when a model or analysis is limited by
existing knowledge and fails to account for emerging or unexplored factors.
For example, screening for low effective mass and wide direct band gap as
shown in Figure 1(a) is appropriate for traditional TCOs that behave as doped
semiconductors following a rigid band model (e.g., ZnO, SnO2). More recently
proposed mechanisms such as polaronic TCs,[9] intrinsic TCs[41], or TCs
containing forbidden transitions[30] are less likely emerge from this
approach, however this can be remedied when new mechanisms are understood and
can be incorporated into the screening.

Once screening criteria are chosen, a threshold bias can arise from selecting
cutoffs, their tolerances, and criteria priorities, as these thresholds often
lack objectivity. For instance, picking a cutoff for “low” hole effective mass
($m^{*}_{\mathrm{h}}$) is typically arbitrary; opting for only the lowest
$m^{*}_{\mathrm{h}}$ could exclude highly transparent materials, and the
$m^{*}_{\mathrm{h}}$ needed to achieve a certain high mobility depends on
material physics. Additionally, there is a lack of threshold standardization
regarding how defect calculations are performed and interpreted. While serial
screening is common, Pareto analysis or multi-objective optimization can
counter threshold bias and improve outcomes.[42] However, one key challenge is
selecting appropriate weighting parameters formally and quantitatively. Such
optimizations become application dependent and are rarely done for p-type TCs.

Lastly, biases can occur when reporting computational results. Novelty biases
can lead to publishing and highlighting new and exciting findings, while
negative or non-novel outcomes are not usually reported. Screening studies
tend to report “new” p-type TC predictions rather than reporting whether past
predictions have been corroborated. However, especially given challenges to
synthesizing predictions in the lab (see next sections), it would be extremely
useful for experimental researchers to know whether a result has been
computationally reproduced by multiple groups and different methods.

Together, these biases can lead to false negatives and false positives
emerging from screenings. Input sets and screening criteria need to be
judiciously selected and appropriately communicated to avoid misleading
predictions. Although false negatives are challenging to address, false
positives can be mitigated by performing a computational “deep-dive” (stage 2)
before attempting synthesis. As an example from our research, shown in Figure
5, sputter synthesis was attempted (stage 3) shortly after ZnZrN2 emerged from
a p-type TC screening (stage 1), before a thorough computational assessment
(stage 2). The target phase could not be synthesized, and by turning back to a
“deep dive” it was hypothesized that due to disorder intolerance the selected
synthesis method would likely have never yielded this phase.[43] In general,
experimentalists using screenings to inform their research should be aware of
how such biases propagate and how to carefully scrutinize the extent of
computational findings before going to the effort to grow it in the lab.

Figure 4: A schematic summary of the primary disconnects in scaling
computationally predicted p-type TCs into solar cell devices.

###  III.2 Predicting synthesizability and stability can be challenging

####  III.2.1 Bulk crystal synthesis

The next disconnect is that many new p-type TCs have been predicted, but not
all have been successfully synthesized in the laboratory in the desired
crystal structure (stage 3 in Figure 3). One likely reason for the lack of
reported synthesis is that many have not been attempted yet, or if attempted
have not been published possibly due to negative results (so-called “dark
reactions”[44]). Computational databases tend to report whether a given
material has been synthesized previously (pointing to the ICSD), and bulk or
powder synthesis is most common. One approach to avoid assessing
synthesizability is to simply restrict input datasets to just these known
experimental materials. However, some exciting new predictions of p-type TCs
do not yet have any reports of synthesis, e.g., Cs$MCh$2 ($Ch$=S,Se,Te)[18]
and double perovskites such as Cs4$M$^2+$B$^3+2$X$^VII12,[45] so for these
materials synthesizability should be judiciously assessed. We will start by
discussing challenges that have arisen with growing hypothetical crystals that
have not yet been synthesized in any form.

Accurate assessment of whether a hypothetical material is actually
synthesizable and stable is not trivial. The first disconnect arises from the
crude assumption that the descriptor $E_{\mathrm{hull}}$ is a good proxy for
synthesizability. Namely, it is generally assumed that materials with
formation enthalpies at or near the ground state energy predicted by DFT at
zero Kelvin ($E_{\mathrm{hull}}\simeq$ 0 eV/atom) are the most likely to be
experimentally realized, while materials with $E_{\mathrm{hull}}\gg$ 0 eV/atom
are less likely to be synthesized.[46, 47, 48] $E_{\mathrm{hull}}$ itself is
impacted by errors in DFT and correction schemes, and does not include
entropic effects. Various low-cost approaches to estimate vibrational and
configurational entropy in computational screenings have emerged,[49, 50]
which can be incorporated screenings. Additionally, assessing dynamic
stability is essential,[51] and synthesis is driven by kinetics as well as
thermodynamics. Despite many recent first-principles and machine learning (ML)
efforts to address kinetics via synthesis pathways,[52, 53, 54, 55, 56, 57]
such as solid-state reaction networks,[58, 59] a deeper understanding and
predictability is still needed; increasing efforts in automating synthesis may
help address this challenge.

Specific synthesizability challenges can arise due to the complexity of
compounds selected as p-type TC candidates.[60] Many predicted TCs are
multinary materials, since multiple elements provide an increased search space
and tunability.[61] For instance, CuAlO2 (often considered the first p-type
TC) can be considered a ternary counterpart to p-type Cu2O in which Al
increases the band gap,[62] and similarly Ba2BiTaO6 a quaternary extension of
p-type BaBiO3 in which Ta increases the gap. Furthermore, binary TCs can be
extended into multinary solid solutions to enhance performance, such as recent
S incorporation into CuI.[63, 64, 65] However, more elements can induce
challenges in synthesizability including increased likelihood to phase
segregate, as well as complex disorder and defect configurations.[60, 48] For
example, one challenge that has stalled progress in thin film synthesis of
predicted p-type TC ZrOS[10] has been phase separation into defective binary
phases ZrO_x and ZrS_x, as it is challenging to introduce S into ZrO_x (see
Figure 5).[37] Another well-known example of such challenges can be observed
in quaternary and quinternary kesterite PV absorbers, which tend to easily
phase separate.[66]

Even if reported as “synthesized” in the ICSD, many compounds are not stable
in air or moisture. For example, K2Sn2O3 was predicted as a p-type TC by
Hautier et al.[10], but rapidly degrades in air. Air instability does not
completely rule out a candidate, but using such materials in a device and even
measuring them introduces serious challenges. For example, SnO and AgI are
unstable in air, but have still been explored for device applications. CuI can
also present stability challenges when exposed to air, presenting variations
in conductivity due to iodine loss or Cu-oxidation. However, this depends
strongly on the fabrication method and solar cell process used. [67, 68, 69]
Non-oxides in particular tend to be less stable in air; some are protected by
a passivation layer, while others can fully decompose or oxidize. The
descriptor “energy above the Pourbaix hull” ($E_{\mathrm{pbx}}$), based on
Pourbaix diagrams, can be used as a thermodynamic check for air stability.[70]
Another recent method uses a “greedy algorithm” to assign heats of oxidation
to screen for in-oxygen environmental stability and heuristically determine
whether a material may self-passivate.[71] However, to our knowledge there is
no extensive benchmarking to show that that these metrics are predictive of
experimental instabilities. In our experience, especially with non-oxide TCs,
p-type TC candidates predicted stable by $E_{\mathrm{hull}}$ and even
$E_{\mathrm{pbx}}$ have still degraded in air. Air instability may be a key
reason why progress in non-oxide p-type TCs has been limited.

####  III.2.2 Thin film synthesis

Specifically for TCs, another set of disconnects arises going from bulk
synthesis to thin film synthesis (stage 3-4). Computational predictions are
generally derived from bulk, periodic calculations at thermodynamic
equilibrium, whereas in practical applications p-type TC materials are thin
films, contain multiple types of defects, and are usually synthesized using
non-equilibrium growth techniques. Sometimes these assumptions hold and, for
example, a predicted structure can be grown as a thin film with properties
corresponding to predictions, as demonstrated by various success stories such
as Ba2BiTaO6 and CaCuP.[15, 72] However, this is not always the case; the
effects of surface energies, surface termination, strain, and texturing in
thin films can influence which phase is stabilized at a given condition, and
can yield a range of properties far beyond the single observable value
predicted for the bulk structure. Additionally, amorphous thin films are
difficult to simulate, though many high-performing n-type TCs are
amorphous.[47]

Similarly, different thin film synthesis methods can result in an extensive
span of structural properties that can deviate from predicted bulk properties.
However, there is not yet a framework such that an ideal synthesis route and
tool can be selected from first principles alone. The synthesis pathway
prediction algorithms mentioned previously are a step in this direction, but
these assume thermodynamic equilibrium and are method agnostic (many
polycrystalline thin film synthesis methods are non-equilibrium). From the
experimental side, the synthesizability challenge is to grow thin films that
can tolerate defects in microstructure, surface effects, grain boundaries,
strain, dislocations, etc., while retaining properties simulated from single
crystalline materials (see next section).[12] In sputtering, a technique
commonly used for exploratory synthesis as well as for commercial TCOs, in
some cases the “effective temperature” at the surface of a growing film have
been shown to be more predictive of synthesizability and structural
properties, rather than the chamber temperature.[43] One disconnect here is
simply that in stages 3 and 4, not all synthesis pathways have been sampled.
Confirmation biases can arise if researchers try one approach, encounter
difficulties, and then decide to abandon the material altogether without
exploring alternative approaches to access a different region of configuration
space.

###  III.3 Origin of the mismatches between theoretical and experimental
optoelectronic properties

If a predicted p-type TC can be successfully synthesized as a thin film (stage
4), the next challenge (stage 5) is to experimentally achieve predicted
properties of interest such as high optical transparency and high hole
mobility and concentration. Yet in some cases of predicted p-type TCs grown in
the lab, at least one of these properties falls short of the computationally
predicted ideal. There may be three reasons for these disconnects: (1)
limitations of high-throughput descriptors in accurately estimating the
optoelectronic properties, (2) differences in material properties between an
ideal bulk crystalline material (the object of computational modeling) and a
practically realizable thin-film material, and (3) extrinsic experimental
issues negatively influencing the predicted properties of a thin-film
material.

####  III.3.1 Accuracy of high-throughput optoelectronic properties

First-principles calculations are useful to approximate experimental
properties, but they also come with caveats. In general more “expensive”
calculations improve accuracy, but there are still limits and trade-offs.
Rather than review computational methods to screen for p-type TCs, which has
been done in depth elsewhere,[12, 13, 73, 2] here we will highlight a few key
limitations to computing transparency, mobility, and dopability in bulk
crystalline materials. We note, however, that the most efficient high-
throughput approaches are built using a tiered screening approach where cheap
methods are used first to select materials (taking into account their lower
accuracy) and more accurate and expensive methods are used for the final
candidates before moving up to experimental verification (see Figure 1).

A wide band gap is a central property of a TC. While “traditional” semilocal
DFT is known to significantly underestimate band gaps (sometimes by 1 to 2
eV),[74] many more accurate methods with different levels of costs are
available nowadays such as GW or hybrid functionals.[9] Some of these methods
have already been used in a high-throughput fashion and machine learning is
speeding up the assessment of high accuracy band gaps with minimal
computational cost.[75, 76, 35, 77] Beyond the accuracy of the method, one
needs to keep in mind optical gaps are not electronic gaps. A better estimate
of the optical gap can be obtained using relatively cheap independent particle
approximation (IPA) computations on top of DFT or hybrids. These computations
will take into account the transition dipole moment for optical absorption
(i.e., the oscillator strength) with possible forbidden or weak transitions.
Some important TCs such as In2O3 rely on forbidden transitions for their high
transparency.[78] Recent high-throughput work including in the TC field has
started including these optical absorption computations, and p-type TCs with
forbidden transitions have recently been proposed.[30] We note that more
expensive approaches beyond IPA such as TD-DFT or Bethe-Salpeter equations
(BSE) can be used to provide more accurate optical absorption, but in general,
TCs with a band-like conduction mechanism have effective masses and band gaps
that lead to small excitonic effects (exciton binding energies of around the
tens of meV).[79]

To model transport, effective masses ($m^{*}$) that can be obtained from DFT
or higher order theories are most commonly used as a proxy for carrier
mobility. While effective masses vary between semilocal DFT and more accurate
methods, the difference is usually small enough for semilocal approaches to
suffice, especially when using $m^{*}$ for screening and as a proxy for
mobility. While $m^{*}$ is a simple concept for conventional single-parabolic
band materials, real materials can be anisotropic and have complex band
structures with several competing bands.[9] Discrepancies between computed
values of $m^{*}$ for the same material in the literature often come from
different crystallographic directions or bands. When used as a proxy for
mobility, it is important to consider all the possible bands involved in
transport and average them out. Otherwise, picking the highest curvature band
tends to underestimate the effective mass. In the last five years, the full
computation of mobility has made enormous progress. Codes such as
Perturbo,[80] EPW[81] and ABINIT[82] have well-documented and tested open
source codes that fully compute phonon-limited mobility. These comprehensive
approaches have been even scaled up and can be used to compute hundreds of
materials automatically.[83] They remain expensive however, and simpler
approaches assuming simplified scattering processes have emerged such as
AMSET, which also provides a high quality open source code.[84] Errors of
around 20% compared to experimental mobilities have been reported for these
approaches based on electron-phonon and Boltzmann transport theory.[84] The
field of TC design has already started to use these methods in recent studies.
[32, 72, 30, 85, 23]

Finally, defects are essential to understand and predict p-type TCs. Point
defects control doping and intrinsic defects can prevent p-type doping.
Certain defects (e.g., oxygen vacancies) can act as “hole-killers” and prevent
p-type doping. Due to the lack of automation and high computational cost,
early high-throughput studies only computed defects as a “deep-dive” step
after low $m^{*}_{\mathrm{h}}$ and wide band gap materials had been
identified.[10] However, since then automation has made huge progress and
software packages have emerged such as pycdt and pylada that facilitate the
generation and processing of input and output files (including charge
corrections).[86, 87] Nowadays, key challenges stem not from automation but
rather from the compromise between expensive, large supercell computations
with hybrid functionals (which is the gold standard for defect computations)
and the cheaper semilocal DFT-based approaches. While semilocal DFT can
provide important information and has helped in first-stage screenings,[36] it
remains essential to always verify these crude methods with a hybrid
functional at the very least for the most promising materials. Indeed, similar
to how semilocal DFT underestimates band gap, it tends to overpredict
dopability, and its use without safeguard has led to false predictions of
p-type doping. The field of high-throughput defects is moving fast with
interesting developments in the use of methods compromising between DFT and
full hybrid such as single-shot hybrid, as well as work on the more exhaustive
search of the defect energy landscape.[88, 89, 90] In all cases, defect
computations remain expensive and proxies have been highly sought out. Some
recent studies have applied practical single-defect-based descriptors early in
the screening, such as the hydrogen interstitial defect descriptor (“FEH”)[21]
or the oxygen-vacancy formation energy descriptor.[91] Other studies have used
branch point energy (BPE, or $E_{\mathrm{BP}}$) as a descriptor,[16, 18] but
we caution against using BPE as its predicting power is questionable.[12]

####  III.3.2 Thin films properties can diverge from bulk properties

Practical TCs are normally required in the form of thin films with thicknesses
on the order of a few hundred nanometers. Even if bulk properties are
appropriately modeled, and if thin films can be grown, this requirement
generates another possible source of disconnects between calculated and
experimental properties (stages 4–5 in Figure 3). Most first-principles
materials modeling methods assume a perfectly crystalline and infinitely
periodic material. However, both assumptions often break down in thin film
TCs, where non-equilibrium and nanoscale effects can dominate.[92]

Depending on the substrate and on the growth process, thin films can be
epitaxial single-crystalline, epitaxial polycrystalline, non-epitaxial
polycrystalline, and amorphous. Moving down this list, an increasing deviation
from the properties of bulk single crystals is typically observed. The current
generation of n-type TCs is either non-epitaxial polycrystalline (ITO, AZO,
FTO) or amorphous (IGZO, ZTO, IZO).[93] Therefore, it is unlikely that
epitaxial single crystalline films can practically be used as p-type TCs, both
because of the much higher cost and the requirement of a suitable substrate,
which is usually dictated by the application (see section “Scalability and
sustainability”).

The TC-relevant property that is most often affected by imperfect
crystallinity is carrier mobility. At a hole concentration of
${10}^{18}\text{\,}\mathrm{c}\mathrm{m}^{-3}$, single-crystalline silicon has
a hole mobility of about
$200\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$, whereas
mobility in polycrystalline Si with $120\text{\,}\mathrm{n}\mathrm{m}$ and
$23\text{\,}\mathrm{n}\mathrm{m}$ grains are about
$10\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$ and
$1\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$,
respectively.[94] At an electron concentration of
${10}^{19}\text{\,}\mathrm{c}\mathrm{m}^{-3}$, typical electron mobilities of
ZnO are $70\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$
for single crystals,
$40\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$ for
epitaxial films, and below
$1\text{\,}\mathrm{c}\mathrm{m}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$ for non-
epitaxial polycrystalline films.[95] While the relative importance of grain
boundary scattering decreases with increasing carrier concentration, grain
boundary scattering can still remain a mobility-limiting mechanism even in the
${10}^{21}\text{\,}\mathrm{c}\mathrm{m}^{-3}$ carrier concentration range,[95]
so it is relevant for TCs. The lower mobility of non-epitaxial polycrystalline
films with respect to their epitaxial counterparts can also be due to a higher
density of both point defects and extended defects. These considerations do
not invalidate the screening approach, with the understanding that the
computationally-determined mobilities are generally upper bounds for the
mobilities achievable by polycrystalline thin-films. Large crystalline bulk
mobility is a necessary but not sufficient requirement as grain boundary
scattering could be an issue.[96, 97] Although grain boundary scattering is
difficult to model from first principles, various simplified high-throughput
approaches have been proposed and applied,[84, 98, 99] and including it in
screenings could be an interesting future avenue of research.

Another property that can be affected by imperfect crystallinity is optical
transparency, especially when the fundamental band gap of the material is
indirect and located at visible (rather than UV) wavelengths. In these cases,
applicability of the material as a TC relies on the weakness of indirect
optical transitions, resulting in negligible absorption in a thin-film sample
because transitions involve both a photon and a phonon. Deviations from
perfect crystallinity due to, e.g., closely spaced grain boundaries, extended
defects, and inhomogeneity, are expected to relax the requirement for phonon
participation in these transitions and, hence, increase the material’s
absorption coefficient. The increase in the absorption coefficient of silicon
between its indirect and direct band gap with decreasing crystalline quality
is a well-known example.[100] A similar effect has recently been observed in
polycrystalline BP and CaCuP films, predicted p-type TCs with direct gaps much
larger than indirect gaps.[72, 28] In both cases, the experimentally reported
absorption coefficient in nanocrystalline films is much higher than the
calculated absorption coefficient using hybrid functionals and electron-phonon
coupling, rendering them non-transparent.

Due to the requirement of inexpensive, large-area thin-film samples for TC in
PV applications, such non-idealities might be unavoidable in practice. One may
argue that that some detrimental effects of polycrystallinity may vanish for
large crystal grain sizes in the $\mu$m range, which can often be achieved in
thin film samples, including In-based TCOs.[97] For some TCs such as ZnO, FTO
and large-grained CuI, optical scattering effects typically become more
prominent with increasing grain size due to an increase in surface roughness.
This is visually manifested as a “haze” effect, which can be beneficial in
thin film PV for light management, but could also result in low
transmittance.[101] For reference, the grain size of commercial ITO films
(n-type TCs) is often below $30\text{\,}\mathrm{n}\mathrm{m}$.[102, 103]

One overarching disconnect between bulk and thin-film materials follows from
the fact that many thin-film growth techniques operate under strongly non-
equilibrium conditions, where thermodynamics-based modeling methods may not be
appropriate. Some examples are plasma-based techniques such as sputter
deposition, pulsed laser deposition, and plasma-assisted chemical vapor
deposition. Interestingly, these are some of the most commonly used deposition
techniques for TCs, partially because the presence of energetic species in the
plasma can promote higher dopant solubility at low temperatures. In these
types of processes where both ions and neutral species are present with a
range of energies, the atomic chemical potentials used in the calculations of
defect formation energies are not easily translated to experimental process
conditions. Thus, the dopability of a TC may be significantly different than
expected. The mobility may also be affected, since it is often limited by
defect scattering in highly doped materials.

Figure 5: Examples of proposed and realized p-type TC candidates across the
stages of their development process (as outlined in Figure 3). Materials (a–i)
have been predicted computationally, while materials (j–l) were discovered
first by synthesis and are included here as references. The materials are
included as follows with the “highlight” column contains key results adapted
from figures from the literature: (a) ZnZrN2 (Woods-Robinson et al.[43]), (b)
ZrSO (Fioretti and Morales Masis[37]), (c) Cs4Cd2SbCl12 (Hu et al.[104]), (d)
BP (Crovetto et al.[28]), (e) CaCuP (Willis et al.[72]), (f) Ta2SnO6 (Barone
et al.[29]), (g) CrMn2O4 (Peng et al.[14]), (h) TaIrGe (Yan et al.[26]), (i)
Ba2BiTaO6 (Dahliah et al.[105]), (j) CuAlO2 (Ismail et al.[106]), (k)
Cu_xZn_1-xS (Woods-Robinson et al.[107]), and (l) CuI (Saranin et al.[108]).

####  III.3.3 Challenges optimizing thin film growth and defects

Early synthesis attempts usually result in low- quality properties, which can
be optimized and tuned with increased research efforts. For example, in the
first reports of thin film n-type ITO in the 1970s conductivity was
approximately 300 S cm-1,[109] but after two decades of intensive research
conductivity was optimized to nearly 2 orders of magnitude higher (with a
record of $\sim$22,000 S cm-1[110]). Since most of the predictions of p-type
TCs have been within the past decade and a given prediction has received far
less attention than ITO, it is reasonable that development of predicted TCs
that have been grown as thin films is stalled between stage 4 and 5 in Figure
3.

Growing a high performance p-type TC requires optimal control of growth
conditions and thus chemical potentials. Many of the computationally
identified candidates offer p-type doping only within a specific window of
chemical potential. For instance, a well-adjusted oxygen chemical potential
can be essential during growth, since highly-reducing conditions can create
oxygen vacancies that compensate any holes. We note that this problem can be
present even if an shallow acceptor is incorporated in the film. Ba2BiTaO6,
for instance, shows limited p-type doping because of the presence of oxygen
vacancies and Bi on Ta anti-sites.[105]

While computational screening uses some level of dopability, screening leads
to focus only on materials that could potentially be p-type doped (i.e., that
could exist in chemical potentials that are favorable for hole doping). There
is not an easy relation between chemical potential and experimental growth
conditions. While qualitative statements can be made — i.e., lower oxygen
chemical potential will be obtained by working in reducing atmosphere —
computations cannot tell what exact conditions (gas flow, temperature, etc.)
to use experimentally. The computed chemical potential can be influenced by
effects not taken into account (alloying, unknown phases or temperature
effects). Similarly, source material impurities should be carefully checked;
e.g., C and Si are dominant impurities in boron sputter targets, but are some
of the most effective dopants in BP (Si impurities alone can lead to carrier
concentrations ${10}^{19}\text{\,}\mathrm{c}\mathrm{m}^{-3}$.[28])

Any single growth technique will have limits in terms of attainable chemical
potentials. A recent case in point is the growth of Ta2SnO6,[29] which has
been computationally identified and reported as a potential p-type TCO (albeit
with a small gap of 2.4 eV) and is highlighted in Figure 5(f).[104, 111] High
quality single crystals grown by molecular beam epitaxy (MBE) did not yield
any measurable conductivity, even after extrinsic doping attempts using K and
Ti. The most likely reason for these failed doping attempts lie in the limits
of oxygen chemical potential achievable in MBE. Oxygen vacancies act as hole-
killers and need to be avoided under oxidizing conditions. However, the most
oxidizing conditions available with this MBE process are still not oxidizing
enough to enable p-type doping. It remains to be seen if other growth
techniques will lead to p-type doping in Sn2TaO6, and similar effects likely
occur in other p-type TC candidates.

For potential TCs belonging to non-oxide families, a common experimental
challenge (besides air stability, discussed earlier) is to ensure low levels
of oxygen contamination in the films. This issue is particularly difficult to
address when the target TC material contains highly oxophilic metals, such as
the ones from Groups 1, 2, 3, 4, and Al. To address this problem, we recommend
including “oxygen tolerance” calculations of promising TC materials, i.e., the
calculated formation energy of oxygen substitutional and interstitial defects
as a function of Fermi level. If any of these defects is found to be a donor
with low formation energy, oxygen contamination is likely to limit the p-type
dopability of the material. The development of more general oxygen-tolerance
principles would also be welcome. For example, one may expect that oxygen
substitution on the anion site is a donor defect in pnictides (one more
valence electron) and an acceptor in halides (one less valence electron). If
this is correct, oxygen incorporation would be detrimental for p-type
conductivity in pnictides but it might be benign or even beneficial in
halides. Beneficial effects of oxygen incorporation have been shown for
CuI.[112, 69] Furthermore, the formation energy of these substitutional
defects might generally be lower in materials containing an anion with a
similar ionic radius to O2-. Thus, one may intuitively expect nitride p-type
TCs to be most negatively affected by oxygen contamination.[113]

As eluded to with these examples, optimization of TCs often involves
introducing defects, disorder and off-stoichiometry in a tunable manner.
Sometimes intrinsic doping is possible, but usually extrinsic dopants are
needed to increase dopability. While most screenings include some
consideration of dopability, often only intrinsic defects are considered, and
detection of problematic hole-killers is targeted without providing the exact
shallow dopant to use. However, in some cases dopants have been proposed,
e.g., for La2SeO2 (Na)[16] BP (Mg, Si, Be, C)[11], Ba2BiTaO6 (K),[105] and
CsCuO (Na, K, Rb),[91]. Our experience is that defect compensation is often
more limiting than the discovery of a shallow acceptor. There are a few rare
cases where, even if a material does not have strong compensation, issues
still arise in discovering the ideal dopant (e.g., n-type La2Sn2O7[114]).

Moreover, screenings usually identify ordered compounds, but the best known
TCs are off-stoichiometric solid solutions rather than dilute defects. For
example, ITO is approximately 5–10 at. % tin, exceeding the dilute limit,[115]
and Al content in Al:ZnO (AZO) is approximately 2%.[116] One workaround is to
screen for cases in which a solid solution of the two compounds is amenable to
high transparency. We have proposed this framework previously, and
incorporated an “alloys database” tool to do so in the Materials Project.[117]
Additionally, in some cases TC properties could emerge non-linearly from
tunability. For example, in p-type NiCo2O4 spinels [118] disorder has been
shown to reduce the hopping barrier and actually lead to increased
conductivity compared to that of the ordered structure.[119] Finding methods
to appropriately model and screen for dopants and tunability early on (stage
1-2) rather than during experimental optimization (stage 5) could lead to
p-type TC breakthroughs, but this has not yet been done in practice.

###  III.4 Novelty bias and confirmation bias

Conventional n-type TCs such as ZnO, ITO, etc., have had their growth
techniques and properties fine-tuned over decades by international research
teams and industries for numerous applications. Thus, it is unsurprising that
the first results of a new predicted material may not have properties
comparable to predictions. But, although it is unfair to compare a preliminary
result with the a highly optimized result of n-type counterparts, this is
still often done, and may lead to discarding a promising material that just
requires optimization to show its real potential. Even if the target material
is not synthesized in a particular study, this does not preclude the
possibility of synthesizing these compounds using other techniques. Yet null
results are usually not reported. This limits the ability for followup
research to learn lessons from less successful results and for results to be
reproduced to increase statistical significance, while increasing the chance
of repeating mistakes and wasting resources. We have highlighted a few key
exceptions; for example, Barone et al. reported being unable to synthesize
conducting phases of Ta2SnO6,[29] but this report inspired follow-up
calculations to suggest their MBE growth did not sample optimal chemical
potentials.[120] To remedy this, amorphous Ta2SnO6 has been proposed,[120] and
these studies inspire future work to explore different regions of phase space.

The initial synthesis of novel computationally-identified thin-film materials
often entails sub-optimal growth conditions, such as chemical potential. This
is particularly true for so-called “high-risk” novel materials, in which
customized tools likely do not yet exist, necessitating makeshift adaptations
of existing experimental tools. Often, growth chambers designed and previously
(or concurrently) used for conventional materials like oxides are repurposed
for the synthesis of multiple new materials. Such adaptations raise concerns
about contamination, especially by volatile species like alkali metals, zinc,
sulfur, or halogens, which can compromise the material’s synthesizability,
quality and defect sensitivity. For example, growing p-type phosphide TCs in
chambers also used for sulfides may adversely affect hole concentration.
“Exploratory” tools such as combinatorial sputter chambers can offer a wide
range of elements and tunability,[121, 122, 123, 124, 125] so they are
particularly advantageous at stage 4 in Fig. 3. However, exploratory thin film
growth chambers accommodating volatile species are not particularly
widespread, which is a barrier to enabling synthesis of new predicted p-type
TCs. Often when predictions are made, one of the limiting factors is finding a
synthesis team willing to risk attempting a novel compound with possibly
exotic elements. In spite of these challenges, new high-throughput materials
synthesis methods are on the rise, and could enable faster progression from a
predicted TC (stage 1) to an optimized thin film (stage 5).[126, 127, 56]

##  IV Lab-to-device disconnects

If a predicted p-type TC can be optimized to high performance in the lab,
incorporating this material into an actual device will bring a set of new
disconnects, some of which are summarized and schematically depicted on the
right of Figure 4. Due to intrinsic disconnects from stages 1–5 and new
interface challenges, so far very few predicted p-type TCs have been
successfully reported as junctions and in solar cells (stages 6–9 in Figure
3). Not only are device reports of predicted TCs rare, but we were unable to
find reports of any p-type TC included as a layer for lateral hole transport,
i.e., “electrode” in (a–c) from Figure 2, even as a small-scale proof-of-
concept cell (stage 7). The only exceptions are NiO_x, which is not
technically a p-type TC due to low hole conductivity (although a recent report
suggested external dopants could increase higher hole transport[128]), and
Cu_xS, which is not very transparent, and in both cases devices have very low
efficiencies.[129] From our understanding, p-type TCs have only been reported
in solar cells as out-of-plane transport layers stacked next to n-type TC
electrode or as continuous metal layers, as in the HTL of Figure 2(d), or as a
semi-transparent bottom contact as in Figure 2(e).

With these limitations in mind, in this section we reflect on some lessons
learned from reported attempts to incorporate p-type TCs into PV-relevant
devices as HTLs, generally following the device configuration shown in Figure
2(e). We use insights from several p-type TCs which were first discovered
experimentally such as Cu_xZn_1-xS, CuI, and delafossites Cu$M$O2, as well as
common non-TC hole transport materials (HTMs) such as NiO_x and common n-type
TCs. Our expectation is that insights will be transferable to computationally
predicted TCs once they can be optimized, and that when advances in material
properties are achieved (stages 1–5), understanding these insights will enable
more rapid scale-up into PV devices (stage 6–9).

Figure 6: Summary of various p-type TCs used as hole transport layers (HTLs)
in laboratory-scale single-junction perovskite solar cells, and (a) a
histogram of the number of cells reported, (b) box plot of reported power
conversion efficiency (PCE), and (c) the range of reported band edges from the
literature. In (a) and (b), color refers to the cell configuration and whether
the HTL is at the top or bottom of the cell (see Figure 2 for details). Data
for this figure has been processed from the Perovskite Database, and contains
studies from 2021 and earlier.[129]

To illustrate the variety of p-type TC contact materials that have been
recently incorporated into PV devices, and the magnitude of research efforts
as well as what is currently lacking, Figure 6(a) depicts a set of inorganic
p-type TCs from the experimental literature which have been implemented as
electrodes or HTLs in perovskite solar cell devices (NiO_x is included as a
benchmark). Data for this plot has been processed from the open access
Perovskite Database.[129] For each reported cell, shading indicates whether it
is are part of a double-layer top contact HTL stack, a double-layer back
contact HTL stack, or a single-layer top electrode; notably, only two binary
materials have been reported as a top electrode, and therefore this is an area
for future research. Perovskite cells have been selected here as
representative since the search for HTLs is an active area of research and
therefore many p-type TCs have recently been attempted; we acknowledge the
high hole mobility requirement of p-type TCs is not usually needed for HTLs
(and some do not need very high transparency), so this test case represents a
specific subset of TC design criteria.

Figure 6(b) plots the distribution or reported power conversion efficiency
(PCE) across cells, and materials are sorted by the highest reported PCE for a
given material. The highest PCE is from delafossites Cu$M$O2 ($M$=Ga, Cr, Fe),
spinel NiCo2O4, mixed anion compounds CuCSN and Cu3PS4, and binaries Cu_xO and
CuI (with various doping schemes). We note that none of these p-type TC
materials were first predicted computationally; to our knowledge to date no
predicted p-type TC has been used as a perovskite HTL. Additionally, these are
results from lab-scale cells, not modules; none of the reported materials
(except for NiO_x) have been reported in a large-area module.

###  IV.1 Challenges at interfaces and junctions

Additional complexity emerges at device interfaces, where a TC is in contact
with other material layers, which can lead to serious optimization challenges.
Before incorporating into a full device, sometimes new contacts are tested on
a heterojunction or a “half-cell” to assess the interface and demonstrate
rectification properties (stage 6). One of the first challenges to emerge at
the interface of a p-type TC and its adjacent layer(s) is interfacial
stability. The interface must be stable without reacting, decomposing or
segregating into unintentional secondary materials or inducing trap states.
For example, although Cu_xZn_1-xS is stable as a thin film, growing it as a
back contact to CdTe solar cells induces complete decomposition of the
Cu_xZn_1-xS layer into CdZnS and CuTe.[107] Although NiO_x is commonly used as
a HTM for perovskite solar cells, at certain processing conditions it has been
shown to react with the perovskite layer and introduce an interfacial defect
layer; similarly, NiO_x in contact with Si in a SHJ cell likely introduces an
interfacial layer of SiO_x. The Materials Project’s interfacial reaction
calculator can be applied as a simple indicator of whether a reaction product
is likely to form thermodynamically at an interface and, if so, the degree of
instability (whether it will likely passivate or decompose). However even if
an interface is thermodynamically stable, it can still degrade over time after
operating in non-equilibrium conditions, such as in light-induced degradation
(LID). For example, the morphology and doping of n-type top contacts can
impact the degree of LID,[130] and, in perovskites, HTMs NiO_x and PEDOT:PSS
degrade upon light exposure,[131] but this effect is nontrivial to predict
without experiments.

When incorporating a novel p-type layer into a PV device, electronic band
edges have to be aligned such that they enable desired junction properties.
Ideally, the VBM of the p-type TC should be aligned to the VBM of the layer it
extracts holes from. In addition, the CBM of the TC should be sufficiently
shallow to form an electron-blocking barrier with the same layer. These
criteria are important in particular for hole selective contacts for thin film
PV, and for p-type layers in tandems. For example, in CdTe solar cells a rule-
of-thumb is that the VBM offset of the p-type contact and CdTe absorber layer
should be within 0.3 eV.[132] Figure 6(c) plots literature-reported band
offsets for p-type TCs used as HTLs in perovskite solar cells, showing that
for most TCs the VBM range is close to the range of various perovskite
absorber layers (-5.42 eV to -5.77 eV, referenced to vacuum[133]). However,
precise band alignment may not be crucial if the TC is placed next to another
highly-doped layer, or if it is a tunneling layer. In SHJ cells it has been
shown that if the hole selective contact is not highly doped, its VB has to be
aligned very close to that of its neighboring layer (within $\sim\pm$0.1 eV)
to enable high efficiencies. On the contrary, if the p-type TC layer is highly
doped above $10^{18}$ cm3, high efficiency can be achieved within a wider
tolerance to VB misalignment.[43]

A combination of X-ray photoemission spectroscopy (XPS) and first principles
slab calculations can be used to assess band offsets of new TCs. Uncertainties
of DFT alignments are less than $\pm$0.3 eV with LDA and GGA-PBE (this is
included as error bars in Figure 6),[134] and this error becomes problematic
if a precise degree of alignment is required between layers. Band offsets can
differ depending on the surface morphology, including crystallographic plane,
surface termination and defects, so there is often a mismatch between computed
bulk band offsets and measured offsets in polycrystalline TCs.[135] Band
offsets are usually not computed early in TC screenings, but sometimes are
assessed in computational deep dives (stage 2). Offsets for an individual
material are not sufficient to simulate band bending when in contact with
another material, so continuum simulations should be also performed to assess
band bending and quasi-Fermi level splitting at a new interface.

In addition to chemical stability and electronic alignment, fabrication of the
p-type TC layer must form strong bonds and mechanically adhere to adjacent
layers. Mechanical breakage at interfaces can induce detrimental voids or
pinholes, which can result in increased electrical resistance and heat losses,
and air or moisture gaps can facilitate degradation. These challenges are
exacerbated in layered non-isotropic p-type TCs such as delafossite CuAlO2, as
highlighted in Figure 5(f) for two CuAlO2-Si heterojunctions,[106] where high
contact resistance due to poor adhesion has been a major barrier to scaling
solar devices. It is expected this may be the case for predicted layered
structures such as [Cu2S2][Ba3Sc2O5].[136]. Some PV applications such as
perovskites are moving towards flexible substrates, so designing p-type TCs
layers to avoid mechanical breakages will become even more important as these
devices come to market.

###  IV.2 Practical solar cell design

When a solar cell is fabricated using a new contact material (stage 7), cell
performance is determined not only by intrinsic material properties but also
by interfacial properties, in particular electronic band alignment, and the
possible presence of interfacial defects. Surface passivation of contacts and
absorbers is in fact an important line of research in thin film and
heterojunction-based PV, however, this topic goes beyond the purpose of this
review.[137, 138] Defects at interfaces can have different origins, from
chemical intermixing to the presence of dangling bonds, among others. However,
the role of these defects are rarely considered in early-stage materials
design. Therefore, an open challenge is the development of screening metrics
to assess interfacial or pinning defects that may arise at contact interfaces,
and how they may limit performance. To assess this, it is important to also
pre-define the type of absorber material and assess its properties
simultaneously. Integrating methods that merge defect calculations and
materials predictions with continuum interface simulations in solar cell
contact layers could provide valuable insights into the behavior of device-
relevant defects under actual operating conditions.[139]

Besides engineering defects, another important consideration for device design
is optimizing fabrication method, including designing for temperature
stability (most devices have a “thermal budget”). A growth method that works
well for synthesizing a single film on a substrate may not be appropriate for
a full device stack. In thin film solar cells, contact layers with low thermal
budgets of a few hundred degrees C are usually preferable; the process
temperature of the TC should not be so high to cause unwanted reactions in the
bulk of the absorber or at the interface. However, many of the synthesis
recipes for the best reported p-type TCs higher temperatures during some step
of the process.[93] For example, Mg-doped CuCrO2 grown by sputtering is one of
the highest performing p-type TCs but requires a synthesis temperature of 750
$\degree$C, which limits its practicality for devices.[3] However, recent
developments have enabled low temperature CuCrO2 synthesis and incorporation
at an HTL in perovskite cells with high efficiency (see Figure 6).[140]
Furthermore, plasma-assisted techniques such as sputtering, pulsed laser
deposition (PLD), and plasma-enhanced chemical vapor deposition (PECVD) are
often used to deposit TCs, and these methods can be harsh on the underlying
material; however, it has recently been shown that oxide TCs can be deposited
on perovskites with very low damage by PLD.[141, 142] P-type TC films for PV
applications that will operate in the sun also need to be stable under UV
irradiation, both in their bulk form and at their interfaces, though this is
less important in superstrate devices in which glass absorbs much of the UV.
It is also important to consider the lifetime of the device when designing
materials to be incorporated, for example whether migration of dopants or
segregation into secondary phases will occur over time.

In PV contacts, the tolerance to non-ideal properties such as electronic
misalignment depends strongly on device architecture. Tolerances of a TC
property within a given device may depend on multiple material and junction
properties and therefore may be better described by a multi-objective
optimization scheme rather than a serial screening “funnel”. For example,
precise band alignment may not be as relevant if the TC is placed next to
another highly-doped layer, or if it is a tunneling layer. In SHJ cells it has
been shown that if the hole selective contact is not highly doped, its VB has
to be aligned very close to that of its neighboring layer (within $\sim\pm$0.1
eV) to enable high efficiencies. However, if the p-type TC layer is highly
doped above $10^{18}$ cm3, high efficiency can be achieved within a wider
tolerance to VB misalignment.[43] Other properties besides alignment also
depend on the device configuration. Perhaps most trivially, the wavelengths to
which a material must be transparent depends on the absorption spectrum of the
absorber layer. Similarly, the high mobility requirement and threshold varies
dramatically across absorber technologies and device designs.

Similar to the novelty bias mentioned previously, one practical challenge when
incorporating new materials into solar cell devices is that device
architectures are optimized around conventional materials. For example, SHJ
solar cells have been highly optimized using a hydrogenated boron-doped
amorphous silicon (B-doped p-a-Si:H) and ITO bilayer as the hole-selective top
contact, with thicknesses and dopings of these layers and adjacent layers
tuned specifically to work well with these materials. Thus, one cannot simply
remove a highly optimized material (e.g., a-Si), insert a new layer (e.g.,
novel p-type TC) into the same device stack, and expect it to deliver optimal
properties, even if that new material has high transparency and conductivity.
Rather, the entire device may have to be re-optimized to accommodate the new
architecture, energetic alignment, and emergent defects from inserting this
new layer. However, this can be tedious when working with a large quantity of
candidates in high-throughput methods, and thus this is not typically done. As
a result a feedback loop ensues: device layers are limited to a small set of
materials (that is, small compared to the size of computational databases)
that are well-optimized, heavily characterized, easy and cheap to grow, and
therefore are more likely to work well in a given device architecture. Then,
more resources are invested in further optimizing these already well-optimized
materials, and new materials such as predicted p-type TCs have a larger
barrier to entry.

###  IV.3 Scalability and sustainability

To our knowledge, a high-performance solar module using p-type TC contacts has
not yet been fabricated. However, if a high-performance p-type TC can someday
be successfully integrated into a small-scale laboratory cell, the next stage
toward commercialization would involve scaling up to full-sized module devices
(stage 9 in Figure 3), which would introduce a new set of challenges. Module
performance tends to lag behind cell performance, namely due to the
requirement for larger surface area deposition and lower tolerance for non-
uniformities.[143] Challenges such as large-area conformity, uniform
crystallinity, and strong adhesion without pinholes are exacerbated for
thinner films,[144] and since contact layers tend to be thinner than absorber
layers in solar cells there is likely less tolerance in contacts for such
nonidealities. TC layers in modules also need to be stable for decades and
maintain high transparency and electrical conductivity while minimizing
resistance losses and avoiding localized performance drops due to defects.

Several deposition methods used regularly to synthesize TCs in lab-based
devices are non-uniform by design and challenging to scale, such as spin
coating and pulsed laser deposition (PLD), though wafer-scale PLD of ITO,
Zr:In2O3, and SnO2 for perovskites or SHJ cells has recently been
demonstrated.[145, 146, 142] Other deposition methods such as sputtering are
regularly adapted for large-area deposition, but require re-optimization and
often result in performance lower than small cells. ITO is usually deposited
by large-area sputtering in SHJ, perovskite, and SHJ-perovskite tandem
modules,[147] while in CdTe modules the n-type top contact FTO is usually
deposited by CVD and can be done by the glass manufacturer [148]. Several
studies have demonstrated large-area fabrication of buffers and contacts such
as NiO_x for perovskite module applications,[149] but scaling CuI and other
inorganic HTLs while maintaining performance remains an open challenge.[150]
Even if physically possible to scale, many high-vacuum synthesis techniques
such as MBE are simply too expensive and labor-intensive to use commercially.
Developing low-cost, module-scale synthesis techniques such as roll-to-roll
printing early on in the p-type TC “design-to-device” development process
could enable more rapid integration into next-generation modules.
Incorporating economic modeling such as techno-economic analysis can help
guide integration of new materials into low-cost devices.[151]

Although the ultimate goal of scaling PV is to mitigate climate change, input
and outputs from production and deployment may cause unintentional
consequences such damage to ecosystems and human health, and there is an
ethical responsibility to minimize harm as we scale new materials such as
TCs.[152, 153] Techniques such as life cycle assessment (LCA) can be used to
identify key sources of negative impacts within a technology’s life cycle and
guide research directions to reduce harm.[154] PV has much lower environmental
impacts than fossil fuel technologies due to negligible inputs and outputs
during operation, but impacts do emerge from mining, refining, manufacturing,
and decommissioning.[155] Mining of critical PV materials can lead to resource
depletion, human health impacts, and ecotoxicity, and has motivated shifting
towards “earth-abundant” materials,[156] such as moving away from indium in
n-type TCs. However, in all PV devices most life cycle GHG emissions are from
the energy intensity of material production rather than mining.[157] In
conventional PV emissions are primarily from refining solar-grade silicon, but
in emerging PV many of these manufacture-stage emissions are also from contact
layers. For example, in perovskite tandems, fabrication of contacts and
buffers such as Spiro-MeOTAD and sputtered ITO contribute significantly to
greenhouse gas (GHG) emissions from the cell.[147] Thin film PV tends to yield
lower impacts compared to Si-based PV,[158, 159] which motivates scaling thin
film PV with high-performing heterojunction contacts, so designing contact
materials with low energy intensity processing could significantly reduce life
cycle impacts.

Historically, LCAs are conducted after technologies reach commercial scale,
which can lead to “technology lock-in” that makes it challenging to replace
harmful materials.[153] While there have been instances of course correction
and phase-out — for example, recent efforts to reduce Co-content in
LiCoO2-based batteries and the formation of NMC in LiNi_$x$Mn_$x$Co_$y$O2 to
address cobalt supply chain hazards (which still rely on lithium and
nickel)[160] — some of this lock-in is unavoidable if we want to rapidly
deploy renewable energy. One such trade-off is that delaying deployment to
wait for PV with lower embodied carbon would result in more emissions than
installing existing PV, so it is important that conventional Si (e.g., PERC)
is rapidly installed.[161]

Low-TRL emerging PV technologies are years or even decades away from
commercialization, but offer serious opportunities for sustainability
improvements. These are technologies in which TCs play an important role, so
designing new contacts for emerging PV with low environmental impacts could
make a big impact. Assessing impacts early in the materials design phase is
challenging,[162] particularly when the synthesis route is unknown, and
especially for p-type TCs since understanding their underlying physics and
design criteria is still lacking. Computational materials discovery often
assumes optimization can reduce impacts, e.g., from replacement of elements or
reduction of processing energy. For example, a computationally-predicted
p-type TC (BeSiP2) has recently been proposed with a new design criteria,
forbidden optical transitions; this material contains Be and is impractical to
scale, but this design criteria may be applicable to an earth-abundant version
that has not yet been discovered,[30] and being too restrictive could lead to
missed opportunities. However, we should keep in mind that there is always a
certain amount of risk with this approach since later-stage replacements can
be difficult. For example, despite significant research efforts, replacing a
toxic element in perovskite absorbers (Pb) with a non-toxic element (e.g., Sn,
Bi) has not yet yielded high enough performance.[163]

The following question arises: how can materials scientists integrate life
cycle thinking throughout the full process of materials discovery and design
to prevent “locking-in” technologies with detrimental impacts? This challenge
calls for the development of early-stage metrics and strategies for embedding
sustainability early in the materials design process. Inspiration can be drawn
from recent approaches like emerging materials risk analysis,[164] “upscaling”
manufacturing assessments,[165] and the framework of anticipatory LCA.[153,
166] Moreover, we encourage the continued development of new metrics —
assessing for embedded carbon, co-product assessment, manufacturing impacts,
and end-of-life considerations — that can be incorporated into the design of
p-type TCs for emerging PV and sustainable materials discovery in general.

##  V Insights for future design of p-type TCs

This article has reviewed various disconnects throughout the materials
discovery process of p-type TCs, spanning from computational design to
material synthesis and from lab-scale devices to scalable sustainable modules.
To address these disconnects to design commercially-relevant p-type
transparent conductors, we propose the following recommendations for materials
designers:

  1. 1.

Improve and benchmark descriptors for p-type TCs, especially for band gap,
dopability, mobility Since the first high-throughput screening for p-type TCOs
appeared a decade ago, first principles computational methods have improved
tremendously and computing power has increased. More accurate assessment of
band gap and mobility are emerging with different levels of accuracy and cost.
Assessment of defects and dopability is still among the most expensive
computations to perform, but they have been automatized and tractable high-
throughput defect approaches are emerging. Predictive low-cost descriptors of
dopability have not yet been developed, and including transport properties
such as grain boundary scattering at early stages is not common, but both of
these pursuits would accelerate screening tremendously. In the last decade
available data from which to screen has also grown tremendously, such as in
the Materials Project and other large databases, and opportunities to use
machine learning to speed up the screening process are naturally emerging from
this data.

  2. 2.

Judiciously assess whether predictions are actually synthesizable. Before lab-
based synthesis, higher-accuracy metrics should be used to assess
synthesizability of emerging screening candidates. To closer approximate
synthesizability of realistic thin film and interface defects, computational
screenings should seek better metrics than $E_{\mathrm{hull}}$ alone. The
ability to move from thermodynamic metrics to assessment of synthesizability
in screenings is an important future frontier of computational materials
design.

  3. 3.

Establish standards and consensus on how defect calculations are run and
interpreted. There are inconsistencies across the literature in terms of which
corrections to use, and qualitative interpretation of formation energies and
Fermi level pinning. To avoid inconsistencies and assist comparison across the
literature, quantitative assessment of carrier concentration is recommended
rather that simply stating whether a material is “dopable.” Following lessons
learned from Hu et al.’s quadruple perovskite study about the “importance of
reasonable band gap description and chemical boundary determination in
predicting defect thermodynamics,”[167] is recommended to standardize running
hybrid defect calculations to confirm p-type dopability.

  4. 4.

Screen for tunability and tolerance ranges, rather than a single compound with
a single property. Tuning stoichiometry and doping in the lab can introduce
effects not typically considered in bulk calculations, so techniques to
simulate tunability would be useful. While very important in practical
materials, the effects of alloying and off-stoichiometry are not often not
taken into account early on in high-throughput studies, so addressing this gap
could lead to new TCs and other material predictions.

  5. 5.

Be judicious whether metrics and materials are device-appropriate. Searches
should be tailored to specific applications, rather than a generic “high-
performance” p-type TC. Some properties matter less than others depending on
application, and by using generic screenings useful targeted materials may be
overlooked. For example, for some devices there may be trade-offs between
properties such that achieving optimal performance is a multi-parameter
optimization problem (e.g., carrier concentration and band alignment can be
coupled). Assessing these trade-offs may require iterative research and
learning between stages 1–9 in Figure 3, and synergizing high-throughput
computation with multi-scale modeling and device testing.

  6. 6.

Develop sustainability and scalability metrics to incorporate early in the
materials design process. To avoid “locking-in” technologies with detrimental
environmental effects, materials scientists should integrate life cycle
thinking throughout the design process, drawing inspiration from recent
methodologies like emerging materials risk analysis and anticipatory life
cycle assessment. Development of early-stage metrics assessing embedded
carbon, co-product impacts, manufacturing impacts, and end-of-life aspects
could help guide sustainable materials discovery in the field of emerging PV
and beyond.

  7. 7.

Conduct more interdisciplinary analyses to bring computed materials into
devices. Historically, separate research teams have performed computations,
synthesis, fabrication, and scale-up modeling, which can lead to communication
barriers. However, there is a recent trend towards connecting theorists,
experimentalists, and analysts on a single project. Ultimately, more
communication and collaboration between scientists across various stages of
materials discovery can help address these disconnects and lead to more
effective design.

The goal of p-type transparent conductor (TC) materials discovery is to
predict a new TC from first principles that can scale up and be used in
devices such as solar panels for more efficient and sustainable technological
development. Although we have made major progress in this field, there is
still a large gap between the research stage of proposing a new candidate and
actually incorporating the candidate into a device. In this perspective, we
have highlighted some major disconnects from the past ten years of research,
as well as insights for future design of p-type TCs. We hope that these
insights can be applied to future design approaches to catalyze a more rapid
and targeted progression in the field of transparent conductors for
sustainable energy technologies.

## Acknowledgments

This work was supported by the U.S. Department of Energy, Office of Science,
Office of Basic Energy Sciences, Materials Sciences and Engineering Division
under Contract No. DE-AC02-05-CH11231 (Materials Project program KC23MP).
R.W.R. was supported by the U.C. Berkeley Chancellor’s Fellowship and the
National Science Foundation (NSF) Graduate Research Fellowship under Grant No.
DGE1106400 and DGE175814, and by University of Washington’s Clean Energy
Institute. G.H. acknowledges support from the U.S. Department of Energy,
Office of Science, Basic Energy Sciences under award number DE-SC0023509. The
work of A.C. was supported in part by a research grant (42140) from VILLUM
FONDEN and co-funded by the European Union (ERC, IDOL, 101040153). Views and
opinions expressed are however those of the authors only and do not
necessarily reflect those of the European Union or the European Research
Council. Neither the European Union nor the granting authority can be held
responsible for them.

##  VI Author contributions

We highlight the author contributions to this study using the CRediT taxonomy.

R.W.R.: Conceptualization, Methodology, Data Curation, Visualization,
Investigation, Writing - Original Draft, Writing – Review & Editing, Funding
Acquisition, Project Administration; M.M.M.: Methodology, Investigation,
Writing – Review & Editing, Funding Acquisition; G.H.: Methodology,
Investigation, Writing – Review & Editing, Funding Acquisition; A.C.:
Methodology, Investigation, Writing – Review & Editing, Funding Acquisition;

## Supplemental Information

The following supplemental data tables are included, and are available via on
the MPContribs platform:

  * •

Computational predictions of p-type TCs

  * •

Experimental reports of predicted p-type TCs

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