一个基于高斯过程的Δ-机器学习方法,用于反应电位能量表面
1Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.
The journal of physical chemistry. A
|October 10, 2023
概括
高斯过程 (GP) 机器学习有效地创建精确的潜在能量表面 (PES). 基于 GP 的 Δ-机器学习 (Δ-ML) 方法提高了化学反应计算的准确性,并减少了化学反应计算中的错误.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 物理化学 物理化学
背景情况:
- 高斯过程 (GPs) 是强大的非参数机器学习工具.
- 一般医生提供不确定性量化,这对于减少复杂模型中的错误至关重要.
- 潜在能量表面 (PES) 对于理解化学反应至关重要.
研究的目的:
- 在Δ-机器学习 (Δ-ML) 框架内应用高斯过程.
- 使用混合方法构建精确的潜在能量表面 (PESs).
- 评估基于GP的Δ-ML对反应系统的效率和准确性.
主要方法:
- 使用高斯过程 (GPs) 来进行Δ-机器学习 (Δ-ML).
- 在低级别的 PES 上使用高级别的 ab initio 计算构建一个纠正 PES.
- 在三个基准反应系统上测试该方法:H + H2,OH + H2和H + CH4.
主要成果:
- 基于GP的Δ-ML方法显示了高水平PES构建的更高效率,而不是直接GP应用.
- 该方法有效地减少了PES计算中的错误.
- 基于GP的 Δ-ML 显示了与基于神经网络的 Δ-ML 方法可比的性能.
结论:
- 斯过程提供了一种高效和准确的方法,可以通过 Δ-机器学习来构建潜在能量表面.
- 基于GP的Δ-ML方法是用于化学反应研究的其他机器学习技术的可行替代方案.
- 这项工作突出了一般医生在计算化学中的实用性,以改进反应动态计算.
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