深度学习密度函数理论 汉密尔顿式用于高效的初始电子结构计算
He Li1,2, Zun Wang1, Nianlong Zou1
1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China.
深度学习现在代表了密度函数理论 (DFT) 哈密尔顿式,加速电子结构计算. 这种DeepH方法为材料科学发现提供了高精度和高效率.
科学领域:
- 计算材料科学科学 计算材料科学
- 量子化学 是一个量子化学.
- 人工智能的人工智能
背景情况:
- 密度函数理论 (DFT) 的计算是计算密集的.
- 当前的方法在电子结构计算中面临着精度-效率的权衡.
研究的目的:
- 开发一个深度神经网络 (DeepH) 来表示DFT哈密尔顿式.
- 在DFT中绕过计算要求很高的自相一致的字段代.
- 为了提高初始电子结构计算的效率.
主要方法:
- 采用了一个传递信息的神经网络框架.
- 该方法利用局部来处理DFT哈密尔顿矩阵的维度和尺寸共变量.
- 深度神经网络被训练来表示晶体材料的哈密尔顿式.
主要成果:
- DeepH方法表现出高精度和高效率.
- 该方法在各种材料系统和物理性质中显示出良好的可转移性.
- 该方法成功地解决了与DFT固有的准确性-效率困境.
结论:
- DeepH在计算材料科学方面取得了重大进展.
- 该方法可以探索大规模的材料系统,包括扭曲的范德瓦尔斯材料.
- 这项工作为更快,更准确的材料发现铺平了道路.
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