机器学习 多体 绿色的功能 对于分子刺激光谱
Christian Venturella1, Christopher Hillenbrand1, Jiachen Li1
1Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.
Journal of chemical theory and computation
|December 27, 2023
概括
本研究引入了一种机器学习 (ML) 框架,用于预测分子绿色的功能,使光辐射光谱和准粒子能量的准确计算. 机器学习模型展示了高效率和跨多种分子系统的可转移性.
科学领域:
- 计算化学计算化学
- 量子力学就是量子力学.
- 机器学习 机器学习
背景情况:
- 在量子多体水平上预测分子性质在计算上要求很高.
- 准确计算格林函数对于理解电子属性至关重要,例如光辐射光谱和准粒子能量.
研究的目的:
- 开发一种机器学习 (ML) 框架,用于预测分子系统的格林函数.
- 为了使光辐射光谱和准粒子能量的有效和准确的计算.
主要方法:
- 核心回归被用来从电子特征预测自能矩阵元素.
- 一个适应对称的基础集被用于旋转不变.
- 分析连续和戴森方程被用来获得实频格林函数.
主要成果:
- 机器学习框架准确地预测了状态密度 (DOS) 和准粒子能量,与多体扰动理论 (GW) 和完整配置相互作用相当.
- ML预测的最高被占和最低未被占的分子轨道能量的平均绝对误差分别为0.13和0.10 eV.
- 该模型在不同分子大小和化学物种中显示出良好的可转移性和数据效率.
结论:
- 拟议的ML框架为计算分子的量子多体性质提供了一种计算效率高,准确的方法.
- 该方法可用于预测较大的有机分子的电子性质,证明其实际实用性.
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