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arXiv 2304.14596physics.chem-ph
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用于高效反应路径优化的鲁棒高斯过程回归方法:在表面过程中的应用

Robust Gaussian Process Regression method for efficient reaction pathway optimization: application to surface processes

Wei Fang, Yu-Cheng Zhu, Yi-Han Cheng, Yi-Ping Hao, Jeremy O. Richardson

AI总结:

针对现有机器学习加速反应路径优化方法的扩展性差、多适用于气相反应的问题,提出结合内坐标与笛卡尔坐标描述符的高斯过程回归框架,显著降低从头算瞬子优化的计算成本。

摘要:

表面过程模拟是计算化学的关键组成部分,可从原子尺度揭示多相催化机制、扩散动力学以及量子隧穿现象。最常见的理论方法涉及反应路径优化,包括半经典隧穿路径(称为瞬子instanton)。然而,这类方法的计算量可能极大,尤其是采用从头算电子结构的瞬子优化时。近年来,机器学习被应用于加速反应路径优化,在广泛应用中展现出巨大潜力。但现有方法存在诸多实际问题,例如描述符规模带来的不利扩展性,且大多针对气相反应设计。我们提出了一种基于高斯过程回归的改进框架,适用于通用变换坐标,可缓解规模问题。该描述符结合了内坐标与笛卡尔坐标,提升了表面过程建模的性能。我们通过三个示例体系中的11个瞬子优化验证表明,新方法大幅降低了从头算瞬子优化的成本,使其开销仅略高于经典过渡态理论计算。

英文摘要:

Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, as well as quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). However, the computational effort can be demanding, especially for instanton optimizations with ab initio electronic structure. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods suffer from practical issues such as unfavorable scaling with respect to the size of the descriptor, and were mostly designed for reactions in the gas phase. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which can alleviate the size problem. The descriptor combines internal and Cartesian coordinates, which improves the performance for modeling surface processes. We demonstrate with eleven instanton optimizations in three example systems that the new approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory calculation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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