模拟分子间相互作用与交换孔双极矩阵分散纠正神经网络潜力的分子间相互作用
Nguyen Thien Phuc Tu1, Siri Williamson1, Erin R Johnson2
1Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada.
The journal of physical chemistry. B
|August 21, 2024
概括
神经网络潜能 (NNP) 提供了准确的,具有成本效益的化学系统建模. 这项工作介绍了机器学习的交换孔双极时刻 (MLXDM) 模型,以将基本的伦敦分散物理纳入NNP.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 神经网络潜力 (NNP) 为化学系统提供了准确和高效的计算.
- 对于伦敦分散等远程相互作用,NNP通常需要进行校正,特别是在密度函数理论 (DFT) 数据上训练时.
- 现有的分散模型可能不太适合与NNP集成.
研究的目的:
- 讨论神经网络潜力的有效分散模型的要求.
- 介绍和突出显示NNP分散校正的机器学习交换孔双极时刻 (MLXDM) 模型.
- 展示MLXDM如何解决NNP中准确的远程物理的需求.
主要方法:
- 利用密度功能理论 (DFT) 原则进行分散校正.
- 开发基于交换孔二极极矩 (XDM) 方法的MLXDM模型.
- 使用神经网络来对XDM计算的原子时刻和极化度进行近似计算.
主要成果:
- MLXDM模型有效地将伦敦分散物理纳入NNP.
- 对XDM至关重要的原子时刻和极化性,可以通过神经网络准确地近似.
- MLXDM模型符合NNP中分散建模的要求.
结论:
- MLXDM模型提供了一个可行的解决方案,用于通过精确的分散相互作用来增强NNP.
- 基于神经网络的原子属性的近似对于分散校正是有效的.
- 这种方法提高了NNP的大规模准确化学模拟的能力.
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