神经量子状态的基于预期-最大化优化,用于Ab Initio量子化学
Shen Fang1, Hongkun Dou1, Zeyu Li1
1School of Astronautics, Beihang University, Beijing 100191, China.
Journal of chemical theory and computation
|May 20, 2025
概括
本研究介绍了基于预期最大化的神经量子状态的优化 (EMO-NQS),这是一种加速量子化学计算的新方法. EMO-NQS显著降低了计算成本,同时保持了原子级化学洞察力的准确性.
科学领域:
- 量子化学 是一个量子化学.
- 计算化学计算化学
- 机器学习在化学中的应用
背景情况:
- 准确的初始量子化学对于原子尺度的理解至关重要.
- 神经量子状态 (NQS) 提供了更好的准确性,但面临着高的计算成本.
研究的目的:
- 开发一种计算效率高的方法来优化NQS.
- 为了加速NQS优化而不会牺牲准确度.
主要方法:
- 建议基于预期最大化的NQS优化 (EMO-NQS).
- 改制了耗时的操作,将其转换为E-step和M-step,以实现成本摊销.
- 整合了基于主动学习 (AL) 的采样方法,以提高稳定性.
主要成果:
- EMO-NQS的优化速度至少是 vanila NQS方法的两倍.
- 与传统的NQS方法相比,准确性保持不变.
- 基于AL的EMO-NQS证明了对分子潜在能量表面结构的持续加速和强大的准确性.
结论:
- EMO-NQS有效地降低了NQS优化的计算成本.
- 该方法为加速量子化学模拟提供了一种可行的方法.
- 积极学习的整合提高了EMO-NQS方法的可靠性.
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