biostudies-literaturePiaggi PMSwiss National Science FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF)DOE | SC | Basic Energy Sciences (BES)Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDOE | SC | Basic Energy Sciencese2207294119https://www.ebi.ac.uk/biostudies/studies/S-EPMC9388152biostudies-literatureUnknown119(33)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 Proceedings of the National Academy of Sciences of the United States of AmericaHomogeneous ice nucleation in an ab initio machine-learning model of water.PMC9388152DE-SC0019394P2ELP2_184382184382Weis JPiaggi PMCar RPanagiotopoulos AZDebenedetti PGfalseHomogeneous ice nucleation in an ab initio machine-learning model of water.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 2022-01-01T00:00:00Z2022 Aug2025-04-04T08:31:20.113Z2025-04-04T08:31:20.113ZS-EPMC93881523593970810.1073/pnas.2207294119