使用图形神经网络和符号回归来建模无序系统
Ruoxia Chen1, Mathieu Bauchy2, Wei Wang3
1Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA. ruoxia@g.ucla.edu.
Scientific reports
|July 2, 2025
概括
本研究引入了一种新的机器学习方法,用于在无序系统中精确的原子间潜在能计算. 这种方法通过克服先前知识和高计算成本的传统局限性来增强分子动力学模拟.
科学领域:
- 计算物理学的计算物理.
- 材料科学是一种材料科学.
- 化学物理 化学物理
背景情况:
- 准确模拟原子轨迹对于使用分子动力学 (MD) 建模无序系统至关重要.
- MD模拟的精度取决于原子间电位函数,该函数控制着原子运动计算.
- 导出原子间潜力的传统方法是知识密集型和计算昂贵的.
研究的目的:
- 为无序系统引入一种新的方法,将机器学习与分子动力学相结合.
- 提供精确的原子间潜在能量计算,提高模拟精度.
- 解决传统的原子间潜能导出方法的局限性.
主要方法:
- 机器学习算法的集成与分子动力学模拟.
- 开发一种用于潜在能源计算的新型计算框架.
- 适用于需要精确的原子轨迹模拟的无序系统.
主要成果:
- 实现了对无序系统的精确原子间潜在能量计算.
- 证明了机器学习综合方法的有效性.
- 克服了对广泛的先前物理知识的需求,并降低了计算成本.
结论:
- 这种新的机器学习辅助方法提供了一种更有效,更准确的方法来建模无序系统.
- 这种方法提升了分子动力学模拟的功能.
- 它为更复杂的材料和现象的更复杂的建模铺平了道路.
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