数据驱动的电子能量的精细化来自两个电子的低密度矩阵理论
Grier M Jones1, Run R Li2, A Eugene DePrince2
1Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States.
The journal of physical chemistry letters
|July 7, 2023
概括
机器学习通过使用三粒子条件来改进电子结构计算,以增强两电子降密矩阵 (v2RDM) 方法. 这种方法显著提高了强烈相关的系统的能量精度.
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.
- 材料科学是一种材料科学.
背景情况:
- 由于指数级缩放,强烈相关的电子带来了计算挑战.
- 减少密度矩阵 (RDM) 方法提供了一种减轻这些成本的方法.
- 变量两电子RDM (v2RDM) 方法受到不完整的N-可表示性约束的限制.
研究的目的:
- 开发一种机器学习 (ML) 协议,以改进v2RDM方法的能源计算.
- 为了提高准确性,利用来自N-可表示性条件的基于物理的特征.
主要方法:
- 作为ML特征,利用了部分三粒子N可表示性条件 (T1和T2) 的违反.
- 仅使用两电子减少密度矩阵 (2RDM) 评估了这些特征.
- 开发了一种ML协议,以改善从v2RDM计算中获得的能量,该计算只执行两个粒子 (PQG) 条件.
主要成果:
- 证明了三粒子N-表示性违规可以有效地作为基于物理的ML特征.
- ML协议显著提高了v2RDM能量计算的准确性.
- 与配置-交互计算的参考值相比,实现了大幅改善的能量.
结论:
- 基于N-可表示性原则的机器学习可以克服v2RDM方法的局限性.
- 这种方法为复杂系统的精确电子结构计算提供了一个有希望的途径.
- 该方法为强相关电子的能量预测提供了显著的增强.
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