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arXiv 2602.19977physics.chem-ph
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高精度分子模拟与机器学习势能和半经典近似量子动力学

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics

University of Basel(巴塞尔大学);ETH Zurich(苏黎世联邦理工学院)

Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O. Richardson

AI总结:

本文提出利用机器学习势能和半经典近似方法,实现高精度且低成本的分子动力学模拟。

摘要:

准确的分子模拟需要高水平的电子结构理论结合严谨的量子动力学近似方法。机器学习方法可以显著降低此流程的计算成本而不损失精度。我们讨论了构建势能面的各种方法,包括迁移学习,其只需要少量昂贵的训练点。通过这种方式,我们可以在高水平上研究化学反应,但成本较低。特别是由于势能是光滑且可微的,它们使能够使用更先进的半经典近似方法来量子动力学,例如扰动修正的瞬间理论,可以捕捉隧穿和非谐振效应。

英文摘要:

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity.

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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