对于非局部潜在能量表面的简单有效的等价信息传递神经网络模型
Yibin Wu1, Junfan Xia1, Yaolong Zhang2
1Heifei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
The journal of physical chemistry. A
|December 12, 2024
概括
我们开发了EquiREANN,一个高效的等效模型用于原子模拟. 这种机器学习潜力准确地描述了非局部交互的最小计算开销,推进了材料建模.
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
- 量子化学是一种量子化学.
背景情况:
- 机器学习潜力 (MLP) 在原子模拟中表现出色.
- 描述局部环境之外的非局部相互作用仍然是MLP的一个挑战.
研究的目的:
- 提出一个简单而高效的等同变量模型,EquiREANN,用于表示非局部潜在能量表面.
- 为了应对在原子模拟中高效建模非局部相互作用的挑战.
主要方法:
- 开发了EquiREANN,这是一个基于消息传递框架的等价模型.
- 使用原子轨道的线性组合作为基本描述符.
- 代更新不变轨道系数和等价轨道函数.
主要成果:
- EquiREANN准确地描述了由于非局部结构变化的潜在能源变化.
- 该模型实现了高精度,计算成本与不变模型相比较.
- 展示了模型捕捉微妙非局部效应的能力.
结论:
- EquiREANN提供了一个有效的解决方案,用于在原子模拟中表示非局部潜在能量表面.
- 拟议的方法提供了一个通用的方法来适应等价信息传递到其他描述符.
- 这项工作提升了机器学习在模拟复杂物质行为方面的能力.
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