超高速电子合估计器:神经网络与基于物理的方法对比
Roohollah Hafizi1, Jan Elsner1, Jochen Blumberger1
1Department of Physics and Astronomy and Thomas Young Centre, University College London, Gower Street, London WC1E 6BT, United Kingdom.
Journal of chemical theory and computation
|June 22, 2023
概括
神经网络为电荷传输提供了精确的电子合估计,优于传统方法. 优化机器学习 (ML) 方法需要更少的数据,改善化学和材料科学中的模拟.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 生物物理学的生物物理.
背景情况:
- 精确的电子合矩阵元素对于模拟电荷转移现象至关重要.
- 现有的基于物理学的方法,如分析重叠方法 (AOM) 有局限性.
研究的目的:
- 调查和比较基于神经网络的合估计器与AOM的性能.
- 为了评估不同的参考数据采样协议,用于机器学习 (ML) 方法.
主要方法:
- 利用神经网络模型来估计电子合.
- 采用了各种数据采样策略:随机,最远点和委员会查询.
- 与已建立的分析重叠方法 (AOM) 进行ML性能比较.
- 引入了使用AOM作为基线的Δ-ML方法.
主要成果:
- 与AOM相比,神经网络估计器的误差,特别是最大误差,较低.
- 机器学习方法所需的训练数据 (数百点) 比以前的机器学习研究 (数千) 少得多.
- Δ-ML 方法在适度的计算开销 (系数为2) 的情况下表现出卓越的性能.
- 灵活的π结合分子由于轨道移位,给ML带来了挑战.
结论:
- 基于神经网络的方法显示出精确的电子合估计的前景.
- Δ-ML 方法提供了精度和计算成本的有效平衡.
- 未来的ML开发应该包含远程描述器,以应对灵活分子的挑战.
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