Fusing high-throughput
quantum mechanical screening techniques
with modern artificial intelligence strategies is among the most fundamental
—yet revolutionary— science activities, capable of opening
new horizons in catalyst discovery. Here, we apply this strategy to
the process of finding appropriate key descriptors for CO2activation over two-dimensional transition metal (TM) carbides/nitrides
(MXenes). Various machine learning (ML) models are developed to screen
over 114 pure and defective MXenes, where the random forest regressor
(RFR) ML scheme exhibits the best predictive performance for the CO2adsorption energy, with a mean absolute error ± standard
deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training
and test data sets, respectively. Feature importance analysis revealedd-band center (εd), surface metal electronegativity
(χM), and valence electron number of metal atoms
(MV) as key descriptors for CO2activation. These findings furnish a fundamental basis for designing
novel MXene-based catalysts through the prediction of potential indicators
for CO2activation and their posterior usage.