Efficient parameterization of transferable Atomic Cluster Expansion for water

TL;DR

This paper introduces a transferable and accurate atomic cluster expansion potential for water, using active learning to efficiently sample configurations and accurately reproduce properties of both ice and liquid water without extensive AIMD simulations.

Contribution

The study presents a novel active learning sampling method for ACE-based potentials, enabling accurate modeling of water across different phases with minimal computational cost.

Findings

Accurately reproduces structural and dynamic properties of water

Uses active learning to select relevant configurations efficiently

Demonstrates transferability from ice to liquid water

Abstract

We present a highly accurate and transferable parameterization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab-initio molecular dynamics (AIMD) simulations. Our results demonstrate that the ACE descriptors enable a potential initially-fitted solely on ice structures which is later upfitted with few configurations of liquid, identified with active learning to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations, while also presenting a transferable interatomic potential for water that reveals the accuracy of first principles reference. This advancement not only enhances our understanding the relationship between ice and liquid water at the atomic level, but also opens up new avenues for studying complex aqueous systems.