Data-driven study of the enthalpy of mixing in the liquid phase

TL;DR

This study systematically evaluates models predicting the enthalpy of mixing in liquid alloys, demonstrating that machine learning models, specifically LightGBM, outperform traditional models and can extend to ternary and multicomponent systems.

Contribution

It introduces a large dataset of enthalpy of mixing measurements and develops a LightGBM machine learning model that improves prediction accuracy over existing models.

Findings

LightGBM model outperforms traditional models in accuracy.

Binary data enables reliable extrapolation to ternary systems.

Muggianu's model overestimates mixing enthalpy in exothermic ternary alloys.

Abstract

The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exist to predict it, but they have never been systematically evaluated. To address this, we collect a large amount of enthalpy of mixing data in binary liquids from a review of about 1000 thermodynamic evaluations. This allows us to clarify the prediction accuracy of Miedema's model which is state-of-the-art. We show that more accurate predictions can be obtained from a machine learning model based on LightGBM, and we provide them in 2415 binary systems. The data we collect also allows us to evaluate another empirical model to predict the excess heat capacity that we apply to 2211 binary liquids. We then extend the data collection to ternary metallic liquids and find that, when mixing is exothermic, extrapolations from the binary systems by Muggianu's model systematically lead to slight overestimations of roughly 10% close to the equimolar composition. Therefore, our LightGBM model can provide reasonable estimates for ternary alloys and, by extension, for multicomponent alloys. Our findings extracted from rich datasets can be used to feed thermodynamic, empirical and machine learning models for material development.