原子神经网络潜力的准确性评估:切断半径和传递信息的影响
Junfan Xia1, Yaolong Zhang2, Bin Jiang1
1Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
The journal of physical chemistry. A
|November 9, 2023
概括
传递信息 (MP) 神经网络通过结合非局部效应来改善原子模拟. 然而,它们对结构和振动属性的准确性在很大程度上取决于切断半径和MP代,而不仅仅是能量和力预测.
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
- 原子模拟的原子模拟.
背景情况:
- 原子神经网络潜能通过近似原子间相互作用来加速模拟.
- 当前的模型经常将相互作用切断到由切断半径定义的局部环境中.
- 传递信息 (MP) 神经网络提供了一个框架,可以在切断半径之外结合非局部交互.
研究的目的:
- 调查切断半径和MP代对模型准确性的影响.
- 分析这些参数对结构和振动特性的影响.
- 评估MP在提高辐射分布函数 (RDF) 和状态振动密度 (VDOS) 预测方面的有效性.
主要方法:
- 采用了具有本地和MP功能的递归嵌入原子神经网络方法.
- 研究了两个代表系统:液态H2O和固体Al2O3.
- 专注于预测RDF和VDOS以评估模型性能.
主要成果:
- 一般来说,MP可以减少能量和力测试的测试误差.
- 如果局部相关性描述不佳,MP可能无法改善RDF和VDOS预测.
- 超出第一个邻近外的切断半径对于MP有效性至关重要.
- 随着代的增加,MP显著提高了准确性,直到趋同.
- 仅仅增加切断半径的效率不如使用MP.
结论:
- 对于结构和振动属性的模型准确性对切断半径和MP设置敏感.
- MP提供了一条有效的路线来提高准确性,特别是在GPU内存方面的考虑.
- 仅仅依靠能量和力的平均测试误差是不足以评估模型的准确性.
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