将深度学习电子结构计算推广到平面波基础
Xiaoxun Gong1,2,3, Steven G Louie4,5, Wenhui Duan6,7,8
1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China.
Nature computational science
|October 3, 2024
概括
研究人员开发了一种新的方法来表示密度函数理论 (DFT) 哈密尔顿人使用深度神经网络. 这种方法弥合了原子轨道 (AO) 和平面波 (PW) 基之间的差距,使更准确的电子结构计算成为可能.
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.
- 科学中的人工智能.
背景情况:
- 深度神经网络 (DNN) 通过学习密度函数理论 (DFT) 显示了电子结构计算的潜力.
- 现有的DNN仅限于原子轨道 (AO) 基础,不包括广泛使用的平面波 (PW) 基础.
研究的目的:
- 开发一种使DNN能够利用平面波 (PW) 基础的DFT结果进行电子结构计算的方法.
- 弥合基于AO的深度学习和基于PW的DFT方法之间的差距.
主要方法:
- 提出了一个真实空间重建方法,直接从PW DFT结果计算AO哈密尔顿矩阵.
- 与传统的基于投影的技术相比,证明了重建方法的效率和准确性.
主要成果:
- 重建方法比传统的投影方法快得多 (数量级).
- 重建的哈密尔顿矩阵准确地复制使用PW基础计算的电子结构.
- 成功地将PW方法的优点 (准确性,灵活性,适用性) 整合到深度学习电子结构方法中.
结论:
- 开发的方法克服了以前用于DFT的DNN的基本限制.
- 允许使用PW DFT创建大规模的高保真训练数据集.
- 为电子结构开发更精确,更广泛的深度学习模型铺平了道路.
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