First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys
Abstract
Multiple principal element alloys (MPEAs) exhibit complex phase equilibria involving multinary solid solutions and intermetallics, which makes it challenging to predict their temperature-composition phase diagrams. Their vast compositional space makes first principles methods prohibitively expensive, while CALPHAD is limited by scarce experimental data. Here, we present a computationally efficient framework to predict the solvus phase boundaries, and hence, phase fields, in refractory MPEAs composed of Cr, Hf, Mo, Nb, Ta, Ti, V, W, and Zr. The approach combines DFT calculated binary mixing enthalpies with sub regular solution models to construct phase diagrams without fitting higher order interactions, enabling efficient scaling across composition space. Validation against 36 binary and 15 ternary phase diagrams demonstrates good agreement, with both experimental results and CALPHAD calculations. We find that the prediction accuracy is enhanced by incorporating lattice dependent energetics through sub regular solution models and including temperature-dependent elemental phase transitions. The framework captures miscibility gaps, solid solution stability, and intermetallic formation, with predicted miscible temperatures typically within 300 K of experimental values. Overall, this work establishes a scalable, first principles based route for highthroughput prediction of phase diagrams in refractory MPEAs. A publicly accessible web interface has also been developed to allow interactive exploration of the predicted phase diagrams, available at https://raptor.engr.wustl.edu.
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BibTeX
@misc{omprakash2026firstprinciplesbased,
title = {{First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys}},
author = {Pravan Omprakash and Nicholas Crnkovich and John Cavin and Nathan Curtis and Adrien Couet and Rohan Mishra},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06627},
eprint = {2609.06627},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06627},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys
AU - Pravan Omprakash
AU - Nicholas Crnkovich
AU - John Cavin
AU - Nathan Curtis
AU - Adrien Couet
AU - Rohan Mishra
PY - 2026
DA - 2026/09/06
PB - arXiv
DO - 10.48550/arxiv.2609.06627
UR - https://arxiv.org/abs/2609.06627
AB - Multiple principal element alloys (MPEAs) exhibit complex phase equilibria involving multinary solid solutions and intermetallics, which makes it challenging to predict their temperature-composition phase diagrams. Their vast compositional space makes first principles methods prohibitively expensive, while CALPHAD is limited by scarce experimental data. Here, we present a computationally efficient framework to predict the solvus phase boundaries, and hence, phase fields, in refractory MPEAs composed of Cr, Hf, Mo, Nb, Ta, Ti, V, W, and Zr. The approach combines DFT calculated binary mixing enthalpies with sub regular solution models to construct phase diagrams without fitting higher order interactions, enabling efficient scaling across composition space. Validation against 36 binary and 15 ternary phase diagrams demonstrates good agreement, with both experimental results and CALPHAD calculations. We find that the prediction accuracy is enhanced by incorporating lattice dependent energetics through sub regular solution models and including temperature-dependent elemental phase transitions. The framework captures miscibility gaps, solid solution stability, and intermetallic formation, with predicted miscible temperatures typically within 300 K of experimental values. Overall, this work establishes a scalable, first principles based route for highthroughput prediction of phase diagrams in refractory MPEAs. A publicly accessible web interface has also been developed to allow interactive exploration of the predicted phase diagrams, available at https://raptor.engr.wustl.edu.
ER -