机器学习潜力与代博尔兹曼倒数:训练实验的实验
Sakib Matin1,2,3, Alice E A Allen2,3, Justin Smith2,4
1Department of Physics, Boston University, Boston, Massachusetts 02215, United States.
Journal of chemical theory and computation
|February 2, 2024
概括
这项研究引入了一种用于机器学习潜力 (MLP) 的新型训练方法,该方法集成了实验数据. 该方法通过使用平衡辐射分布函数纠正MLPs来改进分子动力学模拟.
科学领域:
- 计算材料科学 计算材料科学
- 机器学习在物理学中的应用
- 量子力学模拟的量子力学模拟.
背景情况:
- 在量子力学数据上训练的机器学习潜力 (MLP) 显示出很大的前景.
- 将实验数据整合到现有的MLP培训中是具有挑战性的,因为数据异质.
- 目前的方法很难有效地结合模拟和实验数据.
研究的目的:
- 为 MLP 开发一个结合实验数据的培训程序.
- 为了提高MLP用于分子动力学模拟的准确性.
- 为了解决当前MLP培训方法的局限性.
主要方法:
- 研究了一种基于代博尔兹曼反转的训练程序.
- 使用平衡辐射分布函数数据,对现有MLP的对潜在校正生成.
- 针对纯的基于密度函数理论的MLP进行了校正.
主要成果:
- 修正后的MLP显著减少了在化阶段的过度结构.
- 增强的MLP证明了对实验扩散常数的更好的预测.
- 该方法避免了复杂的程序,如通过分子动力学溶解器自差异化.
结论:
- 介绍了将实验数据集成到MLP中的实用框架.
- 开发的方法提高了分子动力学模拟的准确性.
- 这种方法提供了一种可行的方式,可以利用各种数据源进行材料建模.
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