Fast and accurate estimation of electronic coupling matrix
elements
between molecules is essential for the simulation of charge transfer
phenomena in chemistry, materials science, and biology. Here we investigate
neural-network-based coupling estimators combined with different protocols
for sampling reference data (random, farthest point, and query by
committee) and compare their performance to the physics-based analytic
overlap method (AOM), introduced previously. We find that neural network
approaches can give smaller errors than AOM, in particular smaller
maximum errors, while they require an order of magnitude more reference
data than AOM, typically one hundred to several hundred training points,
down from several thousand required in previous ML works. A Δ-ML
approach taking AOM as a baseline is found to give the best overall
performance at a relatively small computational overhead of about
a factor of 2. Highly flexible π-conjugated organic molecules
like non-fullerene acceptors are found to be a particularly challenging
case for ML because of the varying (de)localization of the frontier
orbitals for different intramolecular geometries sampled along molecular
dynamics trajectories. Here the local symmetry functions used in ML
are insufficient, and long-range descriptors are expected to give
improved performance.