基于神经网络方法对受限制的原子和分子进行无基础研究
Aleksandr S Bedniakov1,2, Denis Bokhan1, Maria M Kolchenko1
1Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia.
The journal of physical chemistry. A
|June 17, 2025
概括
本研究介绍了一种基于神经网络的新型变量量子蒙特卡洛方法,用于分析腔系统. 这种方法消除了对专门基础集的需求,为分子计算提供了更快,更通用的解决方案.
科学领域:
- 计算化学是一种计算化学.
- 量子力学就是量子力学.
- 材料科学是一种材料科学.
背景情况:
- 分析空洞系统通常需要构建符合边界条件的专门基础集.
- 这一过程需要对每个系统,潜力和腔体大小进行耗时的变化优化.
研究的目的:
- 介绍一种用于分析空洞系统的新方法,它绕过了基本集的需求.
- 为不同的腔体大小的分子计算提供一个计算效率高和多功能方法.
主要方法:
- 使用变量量子蒙特卡洛 (VMC) 方法.
- 在VMC框架内实施神经网络方法,以避免基础集构建.
- 将该方法应用于不同大小的腔体中的系统.
主要成果:
- 拟议的方法不需要任何基础设置,使得在各种系统和潜力中实现"即时"应用.
- 取得的结果与小型系统的高度准确的变量估计相当.
- 对许多电子系统的传统高斯基数组方法表现出优越的性能.
结论:
- 基于神经网络的VMC方法为腔系统分析提供了高效和适应性的解决方案.
- 这种方法特别有利于在具有可变腔体大小的系统中研究高压效应.
- 该方法在现有技术上提供了显著的改进,特别是在复杂的多电子系统中.
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