Towards exact molecular dynamics simulations with machine-learned force fields.
Towards exact molecular dynamics simulations with machine-learned force fields.
复制标题
DOI：
10.1038/s41467-018-06169-2
复制
发表时间：
2018-09-24
影响因子：
16.6
通讯作者：
Tkatchenko A
中科院分区：
文献类型：
--
作者：
Chmiela S;Sauceda HE;Müller KR;Tkatchenko A
采用经典力场的分子动力学（MD）模拟构成了当代化学、生物学和材料科学中原子建模的基石。然而，这些模拟的预测能力仅与潜在的原子间势一样好。经典势往往不能忠实地捕捉分子和材料中的关键量子效应。在这里，我们通过将空间和时间物理对称性以自动数据驱动的方式纳入梯度域机器学习（sGDML）模型，从高级从头计算中直接构建灵活的分子力场。所开发的sGDML方法忠实地再现了量子化学CCSD（T）水平的全球力场，并允许使用完全量子化的电子和原子核进行聚合分子动力学模拟。我们目前的MD模拟，灵活的分子多达几十个原子，并提供这些分子的动力学行为的见解。我们的方法提供了实现分子模拟光谱精度的关键缺失成分。同时准确和有效地预测分子性质依赖于结合量子力学和机器学习方法。在这里，作者开发了一种灵活的机器学习力场，具有高水平的分子动力学模拟精度。
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. Simultaneous accurate and efficient prediction of molecular properties relies on combined quantum mechanics and machine learning approaches. Here the authors develop a flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations.
登录
查看更多内容
影响因子：
13.6
作者：
Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
通讯作者：
Ceriotti M
影响因子：
3.7
作者：
Bartok, Albert P.;Kondor, Risi;Csanyi, Gabor
通讯作者：
Csanyi, Gabor
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele
影响因子：
8.4
作者：
Gastegger M;Behler J;Marquetand P
通讯作者：
Marquetand P
影响因子：
3.7
作者：
Artrith, Nongnuch;Urban, Alexander;Ceder, Gerbrand
通讯作者：
Ceder, Gerbrand