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

2025-11-24

AI Summary

A persistent gap exists between computational phase formation predictions and experimental validation for high-entropy alloys (HEAs). Method: This study establishes a closed-loop computational–experimental validation paradigm integrating high-throughput in situ synchrotron X-ray diffraction, density functional theory (DFT) energy calculations, and CALPHAD-based thermodynamic modeling, augmented by a temperature-dependent phase formation probability model and a quantitative confidence metric. Contribution/Results: We identify systematic prediction deviations in FCC/BCC phase stability within Mn-enriched regions—providing a clear direction for model refinement. In the Fe–Ni–Mn–Cr system, the framework achieves strong agreement between computation and experiment across multiple compositions and broad temperature ranges, with >92% phase-type concordance. This significantly enhances the accuracy, reliability, and physical interpretability of HEA phase stability predictions. The methodology constitutes a generalizable framework for rational design of complex multicomponent alloys.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Models for Web evolution

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 °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.

Problem

• Validating phase formability predictions against experimental data in Cantor alloys
• Assessing computational simulation accuracy for high-entropy alloy phase stability
• Identifying discrepancies between predicted and observed FCC/BCC phases in Mn-rich regions

Innovation

• High-throughput synchrotron X-ray diffraction validates alloys
• Quantitative confidence metric evaluates computational predictions
• Temperature-dependent probability model assesses phase formation