Piaggi Pablo M, Panagiotopoulos Athanassios Z, Debenedetti Pablo G, Car Roberto
Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States.
Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
J Chem Theory Comput. 2021 May 11;17(5):3065-3077. doi: 10.1021/acs.jctc.1c00041. Epub 2021 Apr 9.
Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.
机器学习模型正迅速被广泛用于精确模拟复杂的物理化学现象。在此，我们使用这样一种模型以及直接密度泛函理论（DFT）计算来研究水、六方冰（Ih）和立方冰（Ic）的相平衡，着眼于研究冰成核。该机器学习模型基于深度神经网络，并已使用SCAN交换关联泛函获得的DFT数据进行训练。我们使用此模型来驱动增强采样模拟，旨在计算一些DFT驱动模拟无法企及的复杂性质，然后采用适当的重加权程序来计算SCAN泛函的相应性质。这种方法使我们能够计算两种冰多晶型物的熔化温度、成核驱动力、熔化热、熔化温度下的密度、Ih和Ic冰的相对稳定性以及其他性质。我们发现对所有感兴趣的性质都有正确的定性预测。在某些情况下，与实验的定量一致性优于目前最先进的水的半经验势。我们的结果还表明，SCAN正确地预测出Ih冰比Ic冰更稳定。
