Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations,Journal of Chemical Theory and Computation

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Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-12 , DOI: 10.1021/acs.jctc.4c00821
Yi-Fan Hou ₁ , Lina Zhang ₁ , Quanhao Zhang ₁ , Fuchun Ge ₁ , Pavlo O Dral _{1,

2}

Affiliation

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here, we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels–Alder reaction. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster.

中文翻译：

用于加速量子化学模拟的物理信息主动学习

通过构建机器学习势，可以大大加速量子化学模拟，这通常是使用主动学习（AL）来完成的。构建势的实用性通常受到所需的大量努力及其在模拟中鲁棒性不足的限制。在这里，我们介绍了端到端的 AL，用于通过可负担的时间和资源投资以及最小的人为干扰来构建强大的数据高效潜力。我们的 AL 协议基于训练点的物理信息采样、初始数据的自动选择、不确定性量化和收敛监控。该协议的多功能性体现在我们用于模拟振动光谱的准经典分子动力学、关键生化分子的构象搜索以及狄尔斯-阿尔德反应的时间分辨机制中。这些研究花费了我们几天的时间，而不是在高性能计算集群上进行的几周的纯量子化学计算。

更新日期：2024-09-12

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