机器学习辅助选择性配置交互用于准确的地面和激发状态计算
Bastien Casier1, Maissa El Hamdi1, Basile Herzog2
1CNRS UMR 8181 - UCCS Unité de Catalyse et Chimie du Solide, Univ. Artois, Centrale Lille, Univ. Lille, F-62300 Lens, France.
Journal of chemical theory and computation
|January 31, 2026
概括
我们开发了一种机器学习引导的选择性配置交互 (SCI) 方法. 它准确地识别了重要的电子结构决定因素,与分子电子结构计算的最新性能相匹配.
科学领域:
- 量子化学是一种量子化学.
- 计算物理学的计算物理.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的电子结构计算对于理解分子性质至关重要.
- 像全配置交互 (FCI) 这样的传统方法在计算上昂贵.
- 选择性配置交互 (SCI) 方法旨在通过选择重要的电子配置来降低计算成本.
研究的目的:
- 引入一种新的扰动性选择性配置交互 (SCI) 方法.
- 整合一个机器学习分类器,以便有效地选择斯莱特决定因素.
- 以降低计算成本,实现电子结构计算的高精度.
主要方法:
- 由二进制机器学习分类器指导的扰动性SCI方法的开发.
- 利用轻量级的前神经网络 (FNN) 进行快速训练.
- 使用二进制交叉度来识别重要的斯莱特决定因素.
主要成果:
- 拟议的方法的准确性与最先进的SCI方法CIPSI相美.
- 该模型可靠地识别了各种配置空间大小的关键斯莱特决定因素.
- 实现了FCI/CASCI级准确度在10^-4哈特树内,用于地面和激发状态.
- 对于应变的几何形状和形状变化证明了强度.
结论:
- 机器学习引导的SCI方法为电子结构计算提供了一个计算效率高,准确的替代方案.
- 这种方法成功地识别了关键的斯莱特决定因素,从而实现了高精度的预测.
- 该方法显示出开发分子电子结构和潜在能量表面的基于回归的新策略的前景.
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