TY - JOUR
T1 - Homogeneous ice nucleation in an ab initio machine-learning model of water
AU - Piaggi, Pablo M.
AU - Weis, Jack
AU - Panagiotopoulos, Athanassios Z.
AU - Debenedetti, Pablo G.
AU - Car, Roberto
N1 - Funding Information:
P.M.P. is grateful to Carlos Vega, Eduardo Sanz, and Jorge Espinosa for useful feedback on the manuscript. This work was conducted within the center Chemistry in Solution and at Interfaces funded by the US Department of Energy (DoE) under Award DE-SC0019394. P.M.P was also supported by an Early Postdoc.Mobility fellowship from the Swiss National Science Foundation. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the DoE under Contract No. DE-AC05-00OR22725. Simulations reported here were substantially performed by using the Princeton Research Computing resources at Princeton University, which is a consortium of groups including the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology’s Research Computing department.
Funding Information:
within the center Chemistry in Solution and at Interfaces funded by the US Department of Energy (DoE) under Award DE-SC0019394. P.M.P was also supported by an Early Postdoc.Mobility fellowship from the Swiss National Science Foundation. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the DoE under Contract No. DE-AC05-00OR22725. Simulations reported here were substantially performed by using the Princeton Research Computing resources at Princeton University, which is a consortium of groups including the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology’s Research Computing department.
Publisher Copyright:
Copyright © 2022 the Author(s).
PY - 2022/8/16
Y1 - 2022/8/16
N2 - 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.
AB - 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.
KW - density-functional theory
KW - ice nucleation
KW - machine learning
KW - molecular dynamics
KW - water
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U2 - 10.1073/pnas.2207294119
DO - 10.1073/pnas.2207294119
M3 - Article
C2 - 35939708
AN - SCOPUS:85135549353
SN - 0027-8424
VL - 119
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 33
M1 - e2207294119
ER -