A machine learning-based classification approach for phase diagram prediction

TL;DR

This paper presents a machine learning approach to predict phase diagrams in materials science, specifically estimating the number of coexisting phases in ternary systems using thermodynamic descriptors and random forest models.

Contribution

It introduces a novel machine learning classification method that leverages thermodynamic properties to predict phase coexistence in unknown phase diagrams, enhancing materials design efficiency.

Findings

Achieved 84% average accuracy in predicting phase coexistence.

Utilized thermodynamic descriptors and CALPHAD extrapolations for improved predictions.

Demonstrated the method on Al-Cu-Mg-Si-Zn ternary systems.

Abstract

Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unknown phase diagrams based on known phase diagrams using a machine learning-based classification approach. As a proof of concept, we focused on predicting the number of coexisting phases across the 800 K isothermal section of each of the 10 ternaries of the Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction accuracy, we introduced new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using the random forest method, the presence of single-, two-, and three-phase domains was predicted with an average accuracy of 84% across all 10 considered sections with a standard deviation of 11%. The proposed approach represents a promising tool for assisting the investigator in developing new materials and determining phase equilibria efficiently.