Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

TL;DR

This paper introduces an interpretable machine learning framework to classify phases of multi-principal element alloys based on chemical composition, providing insights into phase formation rules and aiding alloy design.

Contribution

It presents a novel local interpretability method combined with clustering to understand ML predictions of alloy phases, enhancing explainability of black-box models.

Findings

Key variables influencing phase formation identified

Model accurately predicts experimental phases

Interactive tool facilitates alloy design

Abstract

There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The novelty of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of novel MPEAs with targeted properties