Homogeneous ice nucleation in an ab initio machine-learning model of water.
Homogeneous ice nucleation in an ab initio machine-learning model of water.
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DOI：
10.1073/pnas.2207294119
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发表时间：
2022-08-16
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
直到最近，由于量子力学计算的计算成本过高，以量子精度模拟冰成核被认为是不可能的。机器学习的最新进展使这些计算变得易于处理，从而大大扩展了基于从头算量子力学理论的分子动力学的应用领域。我们应用这些进展来预测过冷水中冰核的形成速率，并在不依赖于经验力场的情况下研究与成核相关的其他量，尽管调用了经典成核理论的组织框架。这项工作是在更现实的环境中，在化学反应发挥重要作用的条件下，模拟成核过程的一步。分子模拟提供了有价值的深入了解的微观机制下均匀的冰成核。虽然经验模型已被广泛用于研究这一现象，但迄今为止，基于第一原理计算的模拟被证明过于昂贵。在这里，我们通过使用在密度泛函理论能量和力上训练的高效机器学习模型来规避这个困难。我们计算成核率在大气压下，在广泛的过冷，使用播种技术和系统的从头计算精度模拟高达数十万个原子。播种技术提供的关键量是临界簇的大小（即，尺寸使得团簇在给定的过饱和度下具有相等的生长或熔化概率），其与经典成核理论的方程一起用于计算成核速率。我们发现，我们的模型在适度的过冷度的成核率是在我们的计算误差与实验测量吻合得很好。我们还研究了热力学驱动力，界面自由能，堆积无序等性能的影响计算速率。
Until recently, simulating ice nucleation with quantum accuracy was deemed impossible due to the prohibitive computational cost of quantum-mechanical calculations. Recent progress enabled by machine learning has made these calculations tractable and thus greatly extended the field of application of molecular dynamics based on ab initio quantum-mechanical theory. We apply these advances to predict the rate of formation of ice nuclei in supercooled water and to study other quantities relevant to nucleation without relying on empirical force fields, albeit invoking the organizing framework of classical nucleation theory. This work is a step toward modeling nucleation processes in more realistic environments and at conditions in which chemical reactions play an important role. Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.
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影响因子：
--
作者：
HONDOH, T;ITOH, T;HIGASHI, A
通讯作者：
HIGASHI, A
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele
影响因子：
4.4
作者：
Amaya, Andrew J.;Wyslouzil, Barbara E.
通讯作者：
Wyslouzil, Barbara E.
影响因子：
4.4
作者：
Espinosa, J. R.;Navarro, C.;Vega, C.
通讯作者：
Vega, C.
影响因子：
41.2
作者：
del Rosso, Leonardo;Celli, Milva;Ulivi, Lorenzo
通讯作者：
Ulivi, Lorenzo