Data-Driven Discovery of Intrinsic Direct-Gap 2D Materials as Potential Photocatalysts for Efficient Water Splitting

Abstract

Intrinsic direct-gap two-dimensional (2D) materials hold great promise as photocatalysts, advancing the application of photocatalytic water splitting for hydrogen production. However, the time-and resource-efficient exploration and identification of such 2D materials from a vast compositional and structural chemical space present significant challenges within the realm of materials science research. To this end, we perform a data-driven study to find 2D materials with intrinsic direct-gap and desirable photocatalytic properties for overall water splitting. By implementing a three-staged large-scale screening, which incorporates machine-learned data from the V2DB, high-throughput density functional theory (DFT), and hybrid-DFT calculations, we identify 16 direct-gap 2D materials as promising photocatalysts. Subsequently, we conduct a comprehensive assessment of materials properties that are related to the solar water splitting performance, which include electronic and optical properties, solar-to-hydrogen conversion efficiencies, and carrier mobilities. Therefore, this study not only presents 16 2D photocatalysts but also introduces a rigorous data-driven approach for the future discovery of functional 2D materials from currently unexplored chemical spaces.

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