Microscopic Phase-Field Modeling

TL;DR

This paper introduces a bottom-up, atomistically-informed phase-field modeling framework that enhances predictive capabilities for large-scale non-equilibrium processes, demonstrated through ice nucleation simulations.

Contribution

It presents a novel method to construct mesoscopic phase-field models directly from atomistic data, bridging microscopic simulations and field-theoretic approaches.

Findings

Achieved a $10^8$-fold scale-up in ice nucleation simulation

Provided a systematic way to incorporate atomistic details into mesoscopic models

Demonstrated the framework's ability to predict large-scale morphological evolution

Abstract

Phase-field methods offer a versatile computational framework for simulating large-scale morphological evolution. However, the applicability and predictability of phase-field models are inherently limited by their ad hoc nature, and there is currently no version of this approach that enables truly first-principles predictive modeling of large-scale non-equilibrium processes. Here, we present a bottom-up framework that provides a route to the construction of mesoscopic phase-field models entirely based on atomistic information. Leveraging molecular coarse-graining, we describe the formulation of an order parameter-based free energy functional appropriate for a phase-field description via the enhanced sampling of rare events. We demonstrate our approach on ice nucleation dynamics, achieving a spatiotemporal scale-up of nearly $10^8$ times compared to the microscopic model. Our framework offers a unique approach for incorporating atomistic details into mesoscopic models and systematically bridges the gap between microscopic particle-based simulations and field-theoretic models.