High-Throughput Search for Photostrictive Materials Based on a Thermodynamic Descriptor

Photostriction is a phenomenon that can potentially improve the precision of light-driven actuation, the sensitivity of photodetection, and the efficiency of optical energy harvesting. However, known materials with significant photostriction are limited, and effective guidelines to discover new photostrictive materials are lacking. In this study, we perform a high-throughput computational search for new photostrictive materials based on simple thermodynamic descriptors, namely, the band gap pressure and stress coefficients. Using the Δ-SCF method based on density functional theory, we establish that these descriptors can accurately predict intrinsic photostriction in a wide range of materials. Subsequently, we screened over 4770 stable semiconductors with a band gap below 2 eV from the Materials Project database to search for strongly photostrictive materials. This search identifies Te<sub>2</sub>I as the most promising candidate, with photostriction along out-of-plane direction exceeding 8 × 10<sup>-5</sup> with a moderate photocarrier concentration of 10<sup>18</sup> cm<sup>-3</sup>. Furthermore, we provide a detailed analysis of factors contributing to strong photostriction, including bulk moduli and band-edge orbital interactions. Our results provide physical insights into the photostriction of materials and demonstrate the effectiveness of using simple descriptors in high-throughput searches for new functional materials.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2024-11-27
DOI
https://doi.org/10.1021/jacs.4c11484
Citations
19
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.23
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article

High-Throughput Search for Photostrictive Materials Based on a Thermodynamic Descriptor

Yujie Quan, Yubi Chen, Bolin Liao, Zeyu Xiang
19 citations
Journal of the American Chemical Society
Machine Learning in Materials Science
2.23
article

High-Throughput Search for Photostrictive Materials Based on a Thermodynamic Descriptor

Yujie Quan, Yubi Chen, Bolin Liao, Zeyu Xiang
article en
19 citations

Abstract

Photostriction is a phenomenon that can potentially improve the precision of light-driven actuation, the sensitivity of photodetection, and the efficiency of optical energy harvesting. However, known materials with significant photostriction are limited, and effective guidelines to discover new photostrictive materials are lacking. In this study, we perform a high-throughput computational search for new photostrictive materials based on simple thermodynamic descriptors, namely, the band gap pressure and stress coefficients. Using the Δ-SCF method based on density functional theory, we establish that these descriptors can accurately predict intrinsic photostriction in a wide range of materials. Subsequently, we screened over 4770 stable semiconductors with a band gap below 2 eV from the Materials Project database to search for strongly photostrictive materials. This search identifies Te<sub>2</sub>I as the most promising candidate, with photostriction along out-of-plane direction exceeding 8 × 10<sup>-5</sup> with a moderate photocarrier concentration of 10<sup>18</sup> cm<sup>-3</sup>. Furthermore, we provide a detailed analysis of factors contributing to strong photostriction, including bulk moduli and band-edge orbital interactions. Our results provide physical insights into the photostriction of materials and demonstrate the effectiveness of using simple descriptors in high-throughput searches for new functional materials.

Journal of the American Chemical SocietyVol. 146(49)
University of California, Santa Barbara (US)
Openalex Percentile: Top 8%
Machine Learning in Materials Science
2.23
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