通过数据驱动的CASPT2框架,通过机器学习捕获电子相关性
Grier M Jones1,2,3, Konstantinos D Vogiatzis1
1Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States.
Journal of chemical theory and computation
|October 22, 2025
概括
我们引入了一种数据驱动的CASPT2方法来捕获电子相关性. 这种机器学习方法的准确性与传统的CASPT2方法相美.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 多参考扰动理论,包括完整的活性空间二次扰动理论 (CASPT2),对于在电子结构计算中计算电子相关性至关重要.
- 准确描述电子相关性对于预测分子性质至关重要.
研究的目的:
- 引入一种新的数据驱动的CASPT2 (DDCASPT2) 方法来捕获动态电子相关性.
- 评估DDCASPT2在各种系统和基础集大小中的性能.
- 利用机器学习和SHAP分析来提高电子结构计算的准确性.
主要方法:
- 开发基于数据的CASPT2 (DDCASPT2) 方法,利用低级电子结构理论 (Hartree-Fock,CASSCF) 的特征.
- 对DDCASPT2性能进行系统检查,使用不同的系统大小,基础集和两个电子激发的数量.
- 应用夏普利添加式扩张 (SHAP) 分析来解释DDCASPT2模型中使用的基于物理的特征.
主要成果:
- DDCASPT2方法有效地捕捉了动态电子相关性.
- 在多种不同的分子组上评估了性能,在系统大小和基准组变化方面证明了稳定性.
- SHAP分析提供了有关机器学习模型特征的物理相关性的见解.
结论:
- DDCASPT2方法提供了一个可行的基于机器学习的替代传统CASPT2.
- 在动态电子相关性方面,其精度与传统的CASPT2相美.
- 为提高计算化学效率和准确性提供了一个有希望的方向.
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