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.

中文翻译：

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水的从头算机器学习模型中的均匀冰核

分子模拟为了解均质冰成核的微观机制提供了宝贵的见解。虽然经验模型已被广泛用于研究这种现象，但迄今为止，基于第一性原理计算的模拟已被证明非常昂贵。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一困难。我们计算大气压下的成核率，在广泛的过冷范围内，使用种子技术和以从头计算精度模拟的多达数十万个原子的系统。播种技术提供的关键数量是关键簇的大小（即，这样的大小使得簇在给定的过饱和度下具有相等的生长或熔化概率），它与经典成核理论的方程一起用于计算成核率。我们发现我们的模型在适度过冷条件下的成核率在我们的计算误差范围内与实验测量值非常一致。我们还研究了热力学驱动力、界面自由能和堆积无序等特性对计算速率的影响。