基于机器学习势函数的材料力热性质多尺度模拟研究进展

Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential

随着人工智能技术的发展,采用机器学习方法进行势函数的构建和拟合,成为目前解决经验势函数精度问题的主要技术途径。机器学习方法解决了传统势函数拟合中的试错低效问题,已成为材料设计和物性研究不可或缺的有力工具。本文围绕当前机器学习势函数的特点,及其在相变研究、本征性质研究和界面研究等方面的应用,全面总结介绍势函数及其拟合策略,以及其在特定物性研究中的应用,推动机器学习势函数在材料力热性质的多尺度模拟研究。最后,展望了机器学习势函数所面临的挑战和未来发展前景。

With the development of artificial intelligence technology,machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential.Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research.This review represented the characteristics of existing machine learning potential and the applications in phase change,intrinsic properties and interface researches.In addition,the challenge and development trends of machine learning atomic interaction potential were also prospected.