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

TL;DR

This study introduces a high-throughput computational approach using thermodynamic descriptors to discover new photostrictive materials, successfully identifying promising candidates like PtS$_2$ and Te$_2$I with significant photostriction.

Contribution

The paper develops a thermodynamic descriptor-based screening method and applies it to over 4700 semiconductors, discovering new materials with strong photostrictive effects.

Findings

PtS$_2$ and Te$_2$I exhibit photostriction > 10^{-4}

Descriptors accurately predict intrinsic photostriction across materials

Identified key factors influencing strong photostriction

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 constrained density functional theory simulations, we establish that these descriptors can accurately predict intrinsic photostriction in a wide range of materials. Subsequently, we screen 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 PtS$_2$ and Te$_2$I as the most promising ones, with photostriction exceeding 10$^{-4}$ with a moderate photocarrier concentration of 10$^{18}$ cm$^{-3}$. 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 photostriction of materials and demonstrate the effectiveness of using simple descriptors in high-throughput searches for new functional materials.