Abstract
In recent years, machine learning interatomic potentials (ML-IPs) have attracted extensive attention in materials science, chemistry, biology, and various other fields, particularly for achieving higher precision and efficiency in conducting large-scale atomic simulations. This review, situated in the ML-IP applications in cross-scale computational models of materials, offers a comprehensive overview of structure sampling, structure descriptors, and fitting methodologies for ML-IPs. These methodologies empower ML-IPs to depict the dynamics and thermodynamics of molecules and crystals with remarkable accuracy and efficiency. More efficient and advanced techniques from interdisciplinary research field play an important role in opening a wide spectrum of applications spanning diverse temporal and spatial dimensions. Therefore, ML-IP method renders the stage for future research and innovation promising revolutionary opportunities across multiple domains.
摘要
近年来, 机器学习原子势(ML-IP)因其兼顾高精度和高效率的优势, 在材料科学、 化学、 生物学等领域的大尺度原子模拟研究中引起了广泛关注. 本文聚焦于ML-IP在材料跨尺度计算模型中的应用, 全面介绍了ML-IP的结构采样、 结构描述符和拟合方法. 这些方法使ML-IP能够以高精度和高效率模拟分子和晶体的动力学和热力学特性. 跨学科研究领域中更高效、 先进的技术在开拓覆盖不同时间和空间尺度的广泛应用方面发挥着重要作用. 因此, ML-IP方法为未来的研究和创新铺平了道路, 为多个领域带来了革命性的机会.
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Acknowledgements
This work was financially supported by the National Key R&D Program of China (2022YFB3807200), Shanghai Explorer Program (Batch I) (23TS1401500), the National Natural Science Foundation of China (22133005), the Project funded by China Postdoctoral Science Foundation (2022M723276 and GZB20230793), Shanghai Sailing Program (23YF1454900), and Shanghai Post-doctoral Excellence Program (2022660).
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Author contributions Liu J designed the project. Ran N and Qiu W analyzed the data and wrote the manuscript. Yin L edited the figures. All authors contributed to the general discussion.
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Nian Ran received her PhD degree in physical chemistry from the University of Chinese Academy of Sciences in 2022. She works as a postdoctoral researcher at Shanghai Institute of Ceramics, Chinese Academy of Sciences (SICCAS). Her research primarily focuses on utilizing artificial intelligence for materials design.
Wujie Qiu received his PhD degree in theoretical physics from the East China Normal University in 2016. He then worked as a postdoctoral fellow, an assistant research fellow, and later an associate research fellow at SICCAS. His research interests primarily focus on the development of computational electrochemical methods with artificial intelligence and the design of advanced materials.
Jianjun Liu received his PhD degree in physical chemistry from Jilin University in 2002, followed by enriching postdoctoral experiences at the Emory University and Southern Illinois University. In 2011, he joined SICCAS. His research interesting focuses on atom-level material design by various computational methods including classical, quantum, and machine-learning techniques.
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Ran, N., Yin, L., Qiu, W. et al. Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials. Sci. China Mater. 67, 1082–1100 (2024). https://doi.org/10.1007/s40843-023-2836-0
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DOI: https://doi.org/10.1007/s40843-023-2836-0