在水系统中学习电子极化
Arnab Jana1, Sam Shepherd1, Yair Litman2
1Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, U.K.
Journal of chemical information and modeling
|May 28, 2024
概括
大量偏振的统计学学习模型面临由于不连续的原子位置的挑战. 本研究比较了两种方法,发现一种数据驱动的预处理方法有效用于在复杂系统和接口中准确的介电性质建模.
科学领域:
- 计算材料科学科学 计算材料科学
- 在物理学中的统计学学习.
- 量子化学 是一个量子化学.
背景情况:
- 周期系的极化是原子位置的不连续函数,阻碍了统计学学习.
- 对介电性质的准确建模需要强大的极化预测方法.
研究的目的:
- 为了比较两个不同的方法来构建统计学学习模型的批量两极化.
- 为了评估大量水系统和具有挑战性的空气-水接口的模型性能.
- 开发一个计算高效的数据驱动预处理协议用于极化建模.
主要方法:
- 将点电荷模型预处理方法与Wannier中心预测方法进行比较.
- 在散装水系统和空气-水接口上测试模型.
- 使用低级理论衍生物开发数据驱动的预处理协议.
主要成果:
- 这两种方法在散装系统上性能相对较好,点电荷预处理略高,但需要更多的努力.
- 瓦尼耶中心方法准确地预测了空气-水界面的极化,没有修改.
- 数据驱动的预处理协议实现了与现有方法相似的准确性,而不需要明确的点电荷模型.
结论:
- 拟议的数据驱动预处理策略有效地模拟了复杂系统和接口的两极分化.
- 这些训练策略有助于构建精确的极化模型来研究介电性质.
- 这些方法提供了一种途径,可以在研究现实材料时实现初始准确性.
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