Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System

Courtney Kunselman

doi:10.1007/s11669-025-01222-2

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System

2025-11-01

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AI

C

Courtney Kunselman *

S

Siya Zhu

‪

‪Doğuhan Sarıtürk

R

Raymundo Arróyave

DOI：10.1007/s11669-025-01222-2

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This work introduces PhaseForgePlus-a computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting. Using the Pt-W system as an example, we show that the integration of Machine Learning Potentials into the Alloy Theoretic Automated Toolkit can produce physically grounded Gibbs energy descriptions requiring only slight adjustments to produce accurate phase diagrams. Employing the Jansson derivative method in the context of experimental observations, such adjustments can be efficiently and robustly determined through gradient-informed optimization procedures.

Keywords:

Machine learning potentials

CALPHAD

Alloy thermodynamics

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System

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