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Methods and applications of machine learning in computational design of optoelectronic semiconductors

机器学习方法及应用: 光电半导体材料计算设计

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Abstract

The development of high-throughput computation and materials databases has laid the foundation for the emergence of data-driven machine learning methods in recent years. Machine learning has become a crucial methodology propelling researches in computational materials. It has demonstrated tremendous potential in analyzing materials data, expediting materials calculations, predicting material properties, advancing the discovery, screening, and design of new materials. Consequently, an increasing number of methodologies, models, and frameworks of machine learning have emerged. This review provides a comprehensive overview of the latest advancements and applications of machine learning in computational design of optoelectronic semiconductors. We introduce the workflow and strategies of machine learning shallow models, ensemble models, and deep neural networks based on various material representation methods. The associated material databases and toolkits are also discussed. Furthermore, we delve into the applications of these models in predicting material stability, optoelectronic properties, materials inverse design, and establishing relationships between material structures and properties. Finally, we summarize and discuss the key challenges existing in current machine learning, with a specific focus on issues related to the size of available data, data quality, material representation, and materials inverse design.

摘要

摘要高通量计算与材料数据库推动了数据驱动的机器学习方法的发 展. 机器学习已经成为材料计算研究的重要方法, 在分析材料数据、加 速材料计算、预测材料性质、推进新材料发现、筛选和设计等方面展 现出极大的潜力. 众多与材料计算相交叉的机器学习方法、模型以及 框架不断涌现. 本文综述了近年来光电半导体材料计算设计领域内机 器学习方法的最新进展与应用. 介绍了机器学习的流程与类型, 基于不 同材料表示方法的浅层模型、集成模型和深度神经网络, 以及相关材 料数据库和相关工具. 我们还讨论了这些模型在预测材料稳定性与光 电性质、材料逆向设计、构建材料构效关系等方面的应用. 最后, 本文 对目前机器学习方法存在的机遇与挑战, 即数据数量与质量、材料的 表示、材料逆向设计做了进一步总结与讨论.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62125402 and 62321166653).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, 130012, China

    Xiaoyu Yang  (杨晓雨), Kun Zhou  (周琨), Xin He  (贺欣) & Lijun Zhang  (张立军)

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  1. Xiaoyu Yang  (杨晓雨)
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  2. Kun Zhou  (周琨)
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  3. Xin He  (贺欣)
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  4. Lijun Zhang  (张立军)
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Contributions

Author contributions Yang X prepared the manuscript under the direction of Zhang L; Zhou K helped prepare the figures and tables; Zhang L and He X revised the manuscript. All authors contributed to the general discussion.

Corresponding authors

Correspondence to Xin He  (贺欣) or Lijun Zhang  (张立军).

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Additional information

Xiaoyu Yang is a doctoral student at the School of Materials Science and Engineering, Jilin University. He received a Bachelor degree in materials physics from Jilin University in 2020. His research interests focus on promoting the study of new optoelectronic semiconductor materials through high-throughput computation and machine learning.

Xin He obtained her PhD degree at Jilin University (2019), and now is an associate professor and Tang Aoqing Young Scholars of Jilin University. She was awarded the Postdoctoral Innovative Talents Supporting Program in 2019. Her current interests focus on designing novel semiconductor materials for optoelectronic applications.

Lijun Zhang is the Tang Aoqing Distinguished Professor, Dean of the School of Materials Science and Engineering, Jilin University, China. He obtained his BS degree from the Northeast Normal University (2003), and completed his PhD degree at Jilin University (2008), China. He then worked as a postdoctoral researcher at Oak Ridge National Laboratory (2008–2010) and National Renewable Energy Laboratory (2010–2013), and became a research assistant professor at the University of Colorado at Boulder (2013–2014). In September 2014, he became a permanent faculty of the School of Materials Science and Engineering, Jilin University. His current interest focuses on the design of new materials and the enhancement of semiconductor performance tailored for diverse optoelectronic applications.

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Yang, X., Zhou, K., He, X. et al. Methods and applications of machine learning in computational design of optoelectronic semiconductors. Sci. China Mater. 67, 1042–1081 (2024). https://doi.org/10.1007/s40843-024-2851-9

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  • DOI: https://doi.org/10.1007/s40843-024-2851-9

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