Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

TL;DR

This paper explores machine learning techniques to predict precipitate morphologies in alloys by representing free energy surfaces, offering an alternative to traditional phase field models through surrogate optimization and multi-fidelity algorithms.

Contribution

It introduces a novel framework combining machine learning, surrogate optimization, and multi-fidelity modeling to analyze energy landscapes in materials physics, bypassing phase field dynamics.

Findings

Machine learning effectively models high-dimensional free energy surfaces.

Surrogate optimization accelerates the search for equilibrium morphologies.

Multi-fidelity methods improve prediction accuracy and computational efficiency.

Abstract

Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recommender systems and autonomous mobility. There also have been applications to the search for new materials that bear relations to big data classification problems. However, when it comes to physical systems governed by conservation laws, the role of machine learning has been more limited. Here, we present our recent work in exploring the role of machine learning methods in discovering, or aiding, the search for physics. Specifically, we focus on using machine learning algorithms to represent high-dimensional free energy surfaces with the goal of identifying precipitate morphologies in alloy systems. Traditionally, this problem has been approached by combining phase field models, which impose first-order dynamics, with elasticity, to traverse a free energy landscape in search of minima. Equilibrium precipitate morphologies occur at these minima. Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modelling as an alternate framework to explore phenomena controlled by energy extremization. This combination of data-driven methods offers an alternative to the imposition of first-order dynamics via phase field methods, and represents one approach to machine learning materials physics.