Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening

TL;DR

This study uses high-throughput computational screening of over 154,000 materials to identify promising infrared nonlinear optical materials with large shift current responses, aiding future optoelectronic applications.

Contribution

It introduces a multi-step filtering approach combined with first-principles calculations to efficiently discover and analyze high-performance NLO materials in the IR range.

Findings

Identified 32 NLO materials with strong shift current response.

9 compounds exhibit IR shift current peaks, with the highest at 616 μA/V².

Layered structures with C₃v symmetry and heavy p-block elements are particularly effective.

Abstract

Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory, which still largely relies on case by case studies. Here, we employ a high-throughput screening strategy, applying a multi-step filter to the Materials Project database (>154,000 materials), which yielded 2,519 candidate materials for detailed first-principle evaluation. From these calculations, we identify 32 NLO materials with strong shift current response ($\sigma$ > 100 $\mu A/V^2$). Our work reveals that layered structures with $C_{3v}$ symmetry and heavy $p$-block elements (e.g. Te, Sb) exhibit apparent superiority in enhancing shift current. More importantly, 9 of these compounds show shift current response peaks in the IR region, with the strongest reaching 616 $\mu A/V^2$, holding significant application potential in fields such as IR photodetection, sensing, and energy harvesting. Beyond identifying promising candidates, this work establishes a comprehensive and high-quality first-principles dataset for NLO response, providing a solid foundation for future AI-driven screening and accelerated discovery of high-performance NLO materials, as demonstrated by a prototype machine-learning application.