Transforming Materials Discovery for Artificial Photosynthesis: High-Throughput Screening of Earth-Abundant Semiconductors

TL;DR

This paper introduces a high-throughput computational workflow for discovering earth-abundant semiconductor materials optimized for artificial photosynthesis, enabling efficient water splitting and fuel production.

Contribution

It develops a novel ionic translation model trained on ICSD data to predict thousands of new semiconductor compositions with desired properties for solar fuel applications.

Findings

Predicted over thirty thousand new semiconductor compositions.

Identified dozens of promising candidates with optimal properties.

Demonstrated effective screening for redox stability and band gaps.

Abstract

We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that exhibit optimal light-trapping, high efficiency in \ce{H2} and/or \ce{O2} production, and resistance to reduction and oxidation in aqueous media. To achieve this, we use an ionic translation model trained on the Inorganic Crystal Structure Database (ICSD) to predict over thirty thousand undiscovered semiconductor compositions. These predictions are then screened for redox stability under Hydrogen Evolution Reaction (HER) or Oxygen Evolution Reaction (OER) conditions before generating thermodynamically stable crystal structures and calculating accurate band gap values for the compounds. Our approach results in the identification of dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis, offering a significant advancement toward the conversion of sunlight into chemical fuels.