Homogeneous ice nucleation in an ab initio machine learning model of water

Abstract

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 (DFT) 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.

arXiv comment20 pages, 5 figures

References in corpus (29)

• Canonical sampling through velocity-rescaling
• Escaping free-energy minima
• PLUMED 2: New feathers for an old bird
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• Robust Structural Identification via Polyhedral Template Matching
• Accurate determination of crystal structures based on averaged local bond order parameters
• Ab initio theory and modeling of water
• Rethinking Metadynamics: from bias potentials to probability distributions
• The Phase Diagram of a Deep Potential Water Model
• Homogeneous ice nucleation at moderate supercooling from molecular simulation
• freud: A Software Suite for High Throughput Analysis of Particle Simulation Data
• Direct Calculation of Ice Homogeneous Nucleation Rate for a Molecular Model of Water
• A deep potential model with long-range electrostatic interactions
• Homogeneous ice nucleation evaluated for several water models
• Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
• Deep neural network for the dielectric response of insulators
• Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
• Cubic ice Ic free from stacking defects synthesized from ice XVII
• Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional
• Homogeneous ice nucleation in an ab initio machine learning model of water
• Homogeneous nucleation of ice
• The solid-liquid interfacial free energy of close-packed metals: hard spheres and the Turnbull coefficient
• Suppression of Sub-surface Freezing in Free-Standing Thin Films of a Coarse-grained Model of Water
• Calculation of phase diagrams in the multithermal-multibaric ensemble
• Homogeneous Ice Nucleation Rate in Water Droplets
• Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics
• The barrier to ice nucleation in monatomic water
• Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

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