Deep-Learning-Enabled High-Throughput screening of MXene Photocatalyst for Hydrogen Production

Abstract

The challenge in photocatalytic hydrogen production has motivated a targeted search of MXene as a promising class of materials for this transformation because of their high mobility and high light absorption. High-throughput screening has been widely used to discover new materials, but the relatively high cost limits the chemical space for searching MXene. We developed a deep-learning-enabled high-throughput screening approach that identified 14 stable candidates with suitable band alignment for water splitting from 23,857 MXenes. Through the deep learning framework utilizing the layered structure of two-dimensional materials trained on the C2DB, we predict the properties including formation energy, convex hull energy, and bandgap with mean absolute errors of 0.06 eV/atom, 0.06 eV/atom, and 0.14 eV, respectively. Through further density functional theory and non-adiabatic molecular dynamics calculations, we identified a series of descriptors that demonstrate the photocatalytic potential of the candidate MXenes in solar-to-hydrogen efficiency, light absorption, and carrier separation. Using symbolic regression, we proposed a descriptor that captures the relationship between the built-in electric field and the nonadiabatic electron-hole coupling. Notably, the presence of built-in electric fields in Janus MXenes can suppress recombination rates due to spontaneous separation of photogenerated carriers. Our work demonstrates an efficient computational strategy for inversely designing 2D photocatalysts and provides a strategy for designing high-performance photocatalysts for hydrogen production.