通过机器智能发现伴侣矩阵来配合合的潜在能量表面
Yinan Shu1, Zoltan Varga1, Aiswarya M Parameswaran1
1Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
这项研究引入了一种新的神经网络方法,用于装配潜在能量表面. 伴侣矩阵神经网络 (CMNN) 方法准确地表示合的电位能量表面及其梯度,用于化学反应模拟.
科学领域:
- 计算化学计算化学
- 理论化学 理论化学
- 化学物理 化学物理
背景情况:
- 模拟非相应化学反应需要精确的潜在能量表面.
- 传统的方法将表面贴合到一个糖尿病的表征中,以避免形交叉点的复杂性.
- 在adiabatic表示中适应已经面临不连续性和复杂的固有值的挑战.
研究的目的:
- 开发一种替代方法,用于安装合的电潜在能量表面.
- 为了能够准确地分析潜在能量表面和梯度的表示.
- 避免需要基于经验的糖尿病化方案.
主要方法:
- 使用修改后的弗罗贝尼乌斯伴侣矩阵方法.
- 开发了一种新的神经网络架构,用于自动发现特征多项式系数 (CPC).
- 实施了一种对伴侣矩阵进行对角化的程序,以获得亚底动能.
- 这种新方法被称为伴侣矩阵神经网络 (CMNN).
主要成果:
- 该CMNN方法成功地适应了合的亚亚巴特电位能量表面及其梯度.
- 这种方法避免了复杂的固有值和虚假的不连续性.
- 证明了该方法在包括LiH,H3,和硫在内的系统上的有效性.
结论:
- CMNN提供了一种有效和自动化的方法,用于分析潜在能量表面的表示.
- 这种方法简化了非adiabatic化学动态的模拟.
- 提供了一个强大的替代传统的糖尿病适配方案.
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