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在ab initio时代的半经验量子化学数据和可差分编程:I.可差分的分子轨道理论

Martin Stöhr^1,2, Todd J Martínez^1,2

¹Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California CA-94305, United States.

Journal of chemical theory and computation

|January 8, 2026

概括

这项研究提高了使用PyTorch的半经验量子化学 (SQC) 方法,以实现更快,更有效的参数化. 算法差异化提高了量子化学计算的计算成本和稳定性.

科学领域:

计算化学计算化学
量子化学是一个量子化学.
方法开发方法开发

背景情况:

半经验量子化学 (SQC) 方法通过解决有效的福克矩阵提供了快速洞察力.
标准化质量控制方法的参数化传统上具有挑战性,需要广泛的搜索或昂贵的梯度计算.
差异化编程的进步和获得高质量的初始数据的机会为高效的SQC开发提供了新的途径.

研究的目的:

扩展基于PyTorch的SQC方法的实施.
为了提高SQC快速参数化的通用适用性和稳定性.
提高梯度评估中的计算效率和数值稳定性.

主要方法:

在PyTorch.ch中实施全球算法考虑.
确保eigensolver和代自相一致场 (SCF) 程序的一般可区分性.
利用算法差异化进行梯度计算.

主要成果:

计算成本和内存足迹的显著改进.
在梯度评估中增加了数值稳定性.
一个强大的后端,用于高效的SQC参数化.

结论:

增强的PyTorch实现为SQC方法提供了显著的性能和稳定性收益.
这项工作为更高效和更容易获得的量子化学计算提供了基础.
开发的框架促进了SQC方法的快速和可靠的参数化.

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