High-throughput validation of phase formability and simulation accuracy of Cantor alloys

TL;DR

This study combines high-throughput experiments and computational models to evaluate phase stability in Cantor alloys, introducing a confidence metric to assess prediction accuracy and identify discrepancies for model improvement.

Contribution

It presents a novel quantitative confidence metric for comparing computational predictions with experimental data in high-entropy alloys.

Findings

High agreement between predictions and experiments in certain compositional regions.

Identification of discrepancies in FCC/BCC phase predictions at Mn-rich compositions.

Framework for iterative model refinement based on experimental validation.

Abstract

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 {\deg}C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.