Active Learning for Predicting the Enthalpy of Mixing inBinary Liquids Based on Ab Initio Molecular Dynamics

TL;DR

This paper introduces an active learning approach combined with ab initio molecular dynamics to improve predictions of the enthalpy of mixing in binary liquids, especially focusing on refractory elements, enhancing data quality and model accuracy.

Contribution

It presents a novel active learning method to identify critical liquids needing more data, integrating ab initio simulations and clustering analysis for better property prediction.

Findings

Identified need for data on refractory element liquids.

Performed ab initio simulations for 29 alloys.

Enhanced enthalpy of mixing predictions.

Abstract

The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. It can be estimated in a large compositional space from pair wise interactions between elements, for which machine learning has recently provided the most accurate predictions. Further improvements requires acquiring high quality data in liquids where models are poorly constrained. In this study, we propose an active learning approach to identify in which liquids additional data are most needed to improve an initial dataset that covers over 400 binary liquids. We identify a critical need for new data on liquids containing refractory elements, which we address by performing ab initio molecular dynamics simulations for 29 equimolar alloys of Ir, Os, Re and W. This enables more accurate predictions of the enthalpy of mixing, and we discuss the trends obtained for refractory elements of period 6. We use clustering analysis to interpret the results of active learning and to explore how our features can be linked to Miedema's semi empirical theory.