在CASTEP中用于分子动力学的机器学习加速
Tamás K Stenczel1, Zakariya El-Machachi2, Guoda Liepuoniute1
1Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
The Journal of chemical physics
|July 27, 2023
概括
我们开发了一种计算方法,将机器学习 (ML) 潜力与CASTEP模拟集成在一起,从而实现准确的材料建模. 这种方法验证了ML模型与密度函数理论 (DFT) 数据进行可靠的预测.
科学领域:
- 计算材料科学科学 计算材料科学
- 材料建模 材料建模
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 原子间潜力为材料建模提供了希望,但对于新系统通常需要显著的专业知识.
- 已建立的密度函数理论 (DFT) 包被广泛使用,但可以是计算密集的.
- 将ML潜力与第一原则方法相结合,对于推进材料发现至关重要.
研究的目的:
- 介绍一种计算方法,用于将CASTEP模拟软件与机动装配和ML原子间潜力的评估相结合.
- 建立一个框架,通过与DFT参考数据进行定期比较,系统地提高ML的潜在准确性.
- 在材料模拟中展示这种综合方法的实际应用.
主要方法:
- 开发了一个计算框架,将CASTEP,一个第一原则模拟包,与ML原子间潜力模型相结合.
- 在模拟过程中实施了ML潜力的飞行安装和评估方案.
- 使用定期对DFT参考数据进行检查,以确保和量化不断变化的ML模型的准确性.
主要成果:
- 通过CASTEP模拟成功集成ML原子间潜力开发.
- 证明了使用DFT参考数据用于实时ML模型验证和改进的有效性.
- 应用该方法来执行碳纳米结构的高温分子动力学模拟与验证的ML潜力.
结论:
- 提出的计算方法方便在材料建模中使用ML原子间潜力,通过DFT验证确保准确性.
- 这种方法降低了在复杂的模拟中利用ML潜力的进入障碍.
- 免费可用的代码支持计算材料科学和ML应用的学术研究.
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