通过机器学习的电位与远程库伦互动来模拟水的介电电容量
Kehan Cai1, Chunyi Zhang1,2,3, Xifan Wu2,4
1Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA.
The Journal of chemical physics
|November 13, 2025
概括
本研究引入了一种机器学习模型,用于计算在各种电条件下水的介电导电率. 这种新方法准确地预测了水的流量.
科学领域:
- 计算物理和化学 计算物理和化学
- 机器学习在材料科学中的应用.
背景情况:
- 液态水的电介导性对于物理,化学和生物过程至关重要.
- 对水的介电性质的准确建模对于理解其行为至关重要.
研究的目的:
- 开发一个统一的机器学习框架,用于计算水的介电电容.
- 系统地纳入各种电气边界条件 (金属,绝缘,柯克伍德-弗罗利希).
- 研究边界条件对双极波动和介电松的影响.
主要方法:
- 利用了在混合密度函数理论数据上训练的长距离包容性深潜力.
- 采用辅助深度神经网络来预测对介电反应的最大局部化的Wannier函数.
- 研究了三种不同的电极边界条件.
主要成果:
- 展示了计算柯克伍德相关系数,相关长度和介电电容量的一致方法.
- 展示了远程静电学在介电性质中的关键作用.
- 在不同的静电环境中验证了机器学习框架.
结论:
- 建立了一个强大的和可泛化的机器学习框架,用于建模极性液体介电性质.
- 开发的方法在各种静电条件下提供了准确的预测.
- 这项工作推动了液体中介电现象的计算研究.
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