平面波DFT的算法差异化:材料设计,错误控制和学习模型参数
Niklas Frederik Schmitz1,2, Bruno Ploumhans1,2, Michael F Herbst1,2
1Mathematics for Materials Modelling (MatMat), Institute of Mathematics & Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
概括
我们介绍了一个新的框架,它结合了算法分化 (AD) 和密度函数扰动理论 (DFPT),用于材料建模中的准确计算. 这种方法自动化了衍生计算,使逆向设计和参数学习等高级应用程序成为可能.
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.
- 固态物理 固态物理
背景情况:
- 密度功能理论 (DFT) 是材料建模的基石.
- 计算DFT输出对输入参数的衍生值至关重要,但往往很复杂.
- 现有的方法需要手动导出梯度表达式,限制了适用性.
研究的目的:
- 开发一个统一的框架,用于DFT中自动化衍生品计算.
- 结合算法差异化 (AD) 和密度函数扰动理论 (DFPT) 的优势.
- 为了能够准确计算任何DFT输出对任何输入参数的导数.
主要方法:
- 在DFPT框架内实施前式AD方法.
- 将AD-DFPT方法集成到密度功能工具包 (DFTK) 中.
- 通过各种应用程序进行验证,包括反向设计和不确定性传播.
主要成果:
- 该AD-DFPT框架准确地计算导数,而不需要手动梯度导数.
- 在各种材料建模任务中证明了广泛的适用性.
- 成功应用于半导体带间隙的反向设计和学习交换-相关函数参数.
结论:
- 通过自动化衍生计算,AD-DFPT框架显著推进了第一原则材料建模.
- 通过渐变驱动的工作流程开辟新的研究途径.
- 促进复杂的任务,如参数优化和不确定性量化在材料科学.
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