强大的高斯过程回归方法,用于高效地道路径优化:应用到表面过程
Wei Fang1,2,3, Yu-Cheng Zhu4, Yihan Cheng4
1Department of Chemistry, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, P. R. China.
Journal of chemical theory and computation
|May 6, 2024
概括
本研究介绍了一种更快,更稳定的机器学习框架,用于模拟表面过程. 新方法显著降低了化学反应中的初始即时优化计算成本.
科学领域:
- 计算化学是一种计算化学.
- 表面科学是一门科学.
- 理论化学是一种理论化学.
背景情况:
- 模拟表面过程为异质催化,扩散和量子道化提供了原子规模的见解.
- 反应路径的优化,包括半古典道 (瞬间),是计算密集的,特别是与ab initio电子结构计算.
- 现有的用于反应路径优化的机器学习方法面临数值和效率方面的挑战,特别是对于凝聚相反应.
研究的目的:
- 开发一个改进的,高效的,数值稳定的计算框架来模拟表面过程.
- 提高机器学习在模拟化学反应,特别是凝聚相反应中的准确性和适用性.
- 为了降低初始即时优化的计算成本.
主要方法:
- 开发了一个使用高斯过程回归用于一般转换坐标的增强框架.
- 引入了一种结合内部坐标和笛卡尔坐标的新型描述器,用于建模表面过程.
- 将框架应用于三个代表性系统中的11个实例优化.
主要成果:
- 与以前的方法相比,提出的方法显著提高了效率和数值稳定性.
- 新的描述符适用于模拟表面过程.
- 使用改进的框架进行ab initio即时优化在计算上要便宜得多,接近经典过渡状态理论计算的成本.
结论:
- 增强框架为初始即时优化提供了更具成本效益和稳定的方法.
- 这一进步促进了对表面过程和化学反应的更广泛的模拟.
- 该方法显示出在异质催化和凝聚相动力学中的应用潜力很大.
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