预测长期非对话动态所需的神经网络有多复杂?
Hao Zeng1,2,3,4, Yitian Kou1,3,5, Xiang Sun1,2,3,4,6
1Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai, 567 West Yangsi Road, Shanghai 200124, China.
时间序列机器学习准确地预测了非马科夫量子力学. 简单的神经网络在短时间内存方面表现出色,而复杂模型在化学系统中更长时间内存动态方面更好.
科学领域:
- 量子动力学就是量子动力学.
- 凝结相化学 凝结相化学
- 摄影化学的使用.
背景情况:
- 在太阳能转化和光化学过程中,非电动力学是至关重要的.
- 在开放量子系统中,减少密度矩阵的非马科夫动力学需要对先前状态的知识才能进行预测.
- 了解记忆时间是建模这些复杂量子系统的关键.
研究的目的:
- 探索时间序列机器学习,从短时间数据中预测长时间的非adiabatic动态.
- 将机器学习方法与基于物理的转移张量法 (TTM) 进行比较.
- 研究记忆时间对预测准确性的影响,并提出估计有效记忆时间的方法.
主要方法:
- 时间序列机器学习模型的应用:完全连接的神经网络 (FCN),门式循环单元 (GRU) 和卷积神经网络/长短期记忆 (CNN-LSTM).
- 使用Nakajima-Zwanzig概括量子主方程框架来表示非马科夫动态作为线性地图.
- 在自旋玻色子,多态波 (MSH) 模型和胡卜素-氨酸-富勒林三合体系统上测试模型.
主要成果:
- 具有线性映射的FCN模型在具有短内存时间 (旋转玻色子,MSH) 的系统中表现优于非线性模型 (GRU,CNN-LSTM).
- 非线性CNN-LSTM和GRU模型显示,对于具有较长内存时间的三元MSH系统,准确度更高.
- 开发了一种实用的方法来估计在给定的容忍范围内有效的记忆时间.
结论:
- 机器学习模型的选择 (线性与非线性) 取决于量子系统的有效记忆时间.
- 时间序列机器学习为预测非马科夫量子力学提供了一种强大的方法.
- 这些发现为将ML应用于复杂的化学和物理系统提供了指导,特别是在能量转换和光化学方面.
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