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通过自动分化的压力和热流
Marcel F Langer1,2,3, J Thorben Frank1,2, Florian Knoop4
1Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
The Journal of chemical physics
|November 3, 2023
概括
这项研究证明了使用自动差异化的复杂机器学习潜力的高效梯度计算. 该方法准确地预测材料特性,如导热率,增强计算材料科学.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在物理学中的应用
- 固态物理 固态物理
背景情况:
- 机器学习潜能 (MLP) 接近波恩-奥本海默潜能表面,对于材料性能至关重要.
- 精确计算梯度 (力,应力,热流) 对于分子动力学和热传输等模拟是必不可少的.
- 具有高体顺序和等价相互作用的现代MLP需要先进的梯度计算方法.
研究的目的:
- 通过自动分化 (AD) 来计算复杂MLP的力,应力和热流的高效方法.
- 为MLP中的梯度计算提供AD的模型独立实现.
- 验证AD方法并将其应用于预测锡化物等材料的特性.
主要方法:
- 使用自动分化 (AD) 来计算MLP的梯度 (力,应力,热流).
- 开发了适用于各种MLP架构的模型独立实现.
- 在Lennard-Jones潜力和一个等价消息传递神经网络 (MPNN) 上测试了该方法.
主要成果:
- 在复杂的MLP中使用AD证明了高效和准确的梯度计算.
- 通过使用MPNN潜力成功预测了锡化的凝聚性和导热性.
- AD方法克服了手动梯度实现和有限差异方案的局限性.
结论:
- 自动差异化为高级机器学习潜力的梯度计算提供了一个高效和准确的途径.
- 这种方法有助于准确预测关键材料属性,包括热导率.
- 独立于模型的实现增强了MLP在计算材料科学研究中的适用性.
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