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

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 2519 candidate materials for detailed first-principles evaluation. From these calculations, we identify 32 NLO materials with strong shift current response (σ > 100 μA/V2). Our work reveals that layered structures with C3v symmetry and heavy p-block elements (e.g., Te and 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 μA/V2, 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.

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Publication Details

Journal
npj Computational Materials
Published
2026-04-01
DOI
https://doi.org/10.1038/s41524-026-02064-3
Primary Topic
Crystal Structures and Properties
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Qiangqiang Gu, Mingkang Liu, Daye Zheng, Aiqin Yang et al.
npj Computational Materials
Crystal Structures and Properties
article

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

Qiangqiang Gu, Mingkang Liu, Daye Zheng, Aiqin Yang, Dian Jin, Qi Wang, Jian-Hua Jiang
article en

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 2519 candidate materials for detailed first-principles evaluation. From these calculations, we identify 32 NLO materials with strong shift current response (σ > 100 μA/V2). Our work reveals that layered structures with C3v symmetry and heavy p-block elements (e.g., Te and 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 μA/V2, 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.

npj Computational Materials
University of Science and Technology of China (CN), Hong Kong Polytechnic University (HK), National University of Singapore (SG), Suzhou University of Science and Technology (CN), Soochow University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Priority Academic Program Development of Jiangsu Higher Education Institutions, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 28%
Crystal Structures and Properties
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