Accurate melting point prediction through autonomous physics-informed learning

TL;DR

This paper introduces an autonomous, physics-informed learning algorithm for accurately predicting melting points from coexistence simulations, improving reliability and reducing uncertainty in materials property calculations.

Contribution

The method autonomously learns and predicts melting points using physical models and decision-making, enhancing accuracy over existing approaches.

Findings

Successfully predicts melting points with uncertainty estimates.

Identifies significant deviations in literature data.

Demonstrates improved decision-making in simulations.

Abstract

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.