Uncertainty estimation for molecular dynamics and sampling
Uncertainty estimation for molecular dynamics and sampling
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DOI：
10.1063/5.0036522
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发表时间：
2021-02-21
影响因子：
4.4
通讯作者：
Ceriotti, Michele
中科院分区：
文献类型：
--
作者：
Imbalzano, Giulio;Zhuang, Yongbin;Ceriotti, Michele
机器学习模型已经成为一种非常有效的策略，可以避开耗时的电子结构计算，使更大规模、更长时间和更复杂的准确模拟成为可能。考虑到这些模型的内插性，预测的可靠性取决于相空间中的位置，因此对模型训练期间包含的有限数量的参考结构所产生的误差进行估计是至关重要的。当使用机器学习势对有限温度系综进行采样时，单个配置上的不确定性会转化为热力学平均值上的误差，并在模拟进入以前未探索的区域时导致精度损失。在这里，我们讨论如何将不确定性量化与基线能量模型或更稳健但不太准确的原子间势结合使用，以获得更具弹性的模拟并支持主动学习策略。此外，我们引入了一种即时重新加权方案，它使估计从长轨迹中提取的热力学平均的不确定性成为可能。我们提供的例子涵盖了不同类型的结构和热力学性质，以及不同的系统，如水和液态镓。
Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the reliability of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during model training. When using a machine-learning potential to sample a finite-temperature ensemble, the uncertainty on individual configurations translates into an error on thermodynamic averages and leads to a loss of accuracy when the simulation enters a previously unexplored region. Here, we discuss how uncertainty quantification can be used, together with a baseline energy model, or a more robust but less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in thermodynamic averages extracted from long trajectories. We present examples covering different types of structural and thermodynamic properties and systems as diverse as water and liquid gallium.