dxtb-一个高效和完全可区分的框架,用于扩展紧固结合
Marvin Friede1, Christian Hölzer1, Sebastian Ehlert2
1Mulliken Center for Theoretical Chemistry, University of Bonn, Bonn 53115, Germany.
The Journal of chemical physics
|August 9, 2024
概括
我们介绍了dxtb,这是一个Python框架,用于可微分的半实证扩展紧固结合 (xTB) 方法. 它可以有效计算分子性质,并将量子化学与机器学习相结合.
科学领域:
- 计算化学的计算化学
- 机器学习 机器学习
- 量子化学 是一个量子化学.
背景情况:
- 自动区分 (AD) 对于机器学习优化至关重要.
- 现在,AD的实用性已经在量子化学中被认可,用于导数计算.
- 半经验扩展紧密结合 (xTB) 方法提供了准确性和计算成本的平衡.
研究的目的:
- 介绍dxtb,一个开源的,完全可区分的xTB方法框架.
- 为了实现分子性质的高效计算,并促进机器学习的整合.
- 为以物理为灵感的端到端可微分模型提供基础.
主要方法:
- 在Python中开发,使用PyTorch进行数组运算.
- 实现了全面的代码向量化和优化,以提高计算效率.
- 杆自动区分用于计算任意顺序的衍生品.
主要成果:
- dxtb的性能与小分子编译的xTB程序相美.
- 能源评估与现有计划是一致的.
- 自动差异化核衍生品比分析类衍生品慢2-5倍.
- 在计算分子和光谱性质方面证明了实用性.
结论:
- dxtb简化了量子化学中的优化和属性评估.
- 它促进了半经验量子化学与机器学习的无整合.
- 该框架推进了半经验方法,并支持混合机器学习应用程序.
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