这是GradDFT. 一个用于机器学习的软件库,增强密度函数理论
Pablo A M Casares1, Jack S Baker1, Matija Medvidović1,2,3
1Xanadu, Toronto, Ontario M5G2C8, Canada.
The Journal of chemical physics
|February 13, 2024
概括
这项研究介绍了GradDFT,一个机器学习增强的计算化学库. 它提高了使用神经网络的复杂系统的密度函数理论 (DFT) 精度,为材料科学研究提供了一个新的工具.
科学领域:
- 计算量子化学 计算量子化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 密度函数理论 (DFT) 被广泛使用,但对于高度相关的系统缺乏准确性.
- 机器学习 (ML) 提供了增强DFT能力的潜力,但也面临着挑战.
研究的目的:
- 为 ML 增强的 DFT 开发一个可差异化的库.
- 为了实现快速的原型设计和实验新型交换相关函数.
主要方法:
- 介绍了GradDFT,一个基于JAX的,完全可差异化的DFT库.
- 使用神经网络开发了交换相关函数的新型参数化.
- 为培训和基准测试编制了一组实验性二元分离能量的数据集.
主要成果:
- 在不同系统中展示了ML增强的函数的泛化能力.
- 评估了训练数据噪声对模型准确性的影响.
- 展示了用于DFT计算的可微分,自我一致的代程序.
结论:
- GradDFT促进了准确的ML增强的DFT函数的开发.
- 这种方法对改善强烈相关系系统的计算具有前景.
- 进一步的研究可以利用GradDFT来推进计算材料科学.
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