通过端到端可差分的原子模拟优化力场优化
Abhijeet Sadashiv Gangan1, Ekin Dogus Cubuk2, Samuel S Schoenholz3
1Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States.
这项研究引入了可微分的模拟,以优化原子力场. 这种方法准确地捕获复杂的材料特性,如弹性和振动,提高模拟的准确性和通用性.
科学领域:
- 计算材料科学 计算材料科学
- 原子模拟的原子模拟.
- 部队现场发展部队现场发展
背景情况:
- 精确的原子模拟依赖于精确的力场.
- 传统的参数优化方法与复杂的材料属性作斗争.
- 当前的机器学习方法通常集中在能量和力量上,限制了属性预测.
研究的目的:
- 开发一个框架,利用可微分模拟来优化原子力场.
- 为了能够准确地预测超出能量和力之外的复杂材料属性.
- 为了提高材料模拟力场的精度和通用性.
主要方法:
- 实现了一个框架,内部循环模拟和外部循环优化.
- 在属性预测和力场优化中利用自动差异化进行分析梯度计算.
- 优化经典潜力 (斯蒂林格-韦伯,EDIP,BKS) 和机器学习的潜力.
主要成果:
- 成功复制了弹性常数,状态的振动密度,以及和SiO2.2的声子分散.
- 微调的机器学习潜力,以准确预测辐射分布函数.
- 与传统方法相比,实现了对未见温度的更高的准确性和通用性.
- 同时对多个目标属性的优化证明.
结论:
- 微分模拟为推进材料理解提供了强大的工具.
- 分析梯度计算提高了力场优化的效率和准确性.
- 开发的框架为材料科学中的理论探索和实际应用提供了一种多功能方法.
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