Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

中文翻译：

---

借助机器学习的力场进行精确的分子动力学模拟。

利用经典力场的分子动力学（MD）模拟构成了化学，生物学和材料科学领域当代原子建模的基石。但是，这些模拟的预测能力仅与潜在的原子间电势一样好。经典势能常常无法如实地捕获分子和材料中的关键量子效应。在这里，我们通过以自动数据驱动的方式将空间和时间的物理对称性合并到梯度域机器学习（sGDML）模型中，从而可以从高层从头算开始直接构建灵活的分子力场。发达的sGDML方法忠实地再现了量子化学CCSD（T）精度水平下的整体力场，并允许使用完全量化的电子和原子核进行聚合的分子动力学模拟。我们提供了多达数十个原子的柔性分子的MD模拟，并提供了有关这些分子的动力学行为的见解。我们的方法为实现分子模拟中的光谱准确性提供了关键的缺失要素。