62 citations · 73 across the 6 of their papers we have counts for
5 papers · 1 filter
From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions
Courtney Kunselman, Doguhan Sariturk, Siya Zhu +2
Identifying regions of design space subject to spinodal decomposition is a critical component of alloy design in high-dimensional composition spaces. In cases where designers are s…
Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System
Courtney Kunselman, Siya Zhu, Doguhan Sariturk +1
This work introduces PhaseForgePlus -- a computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting. Using the Pt-…
Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
Siya Zhu, Doguhan Sariturk, Raymundo Arroyave
High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase st…
Analytical Gradient-Based Optimization of CALPHAD Model Parameters
Courtney Kunselman, Brandon Bocklund, Richard Otis +1
The calibration of CALPHAD (CALculation of PHAse Diagrams) models involves the solution of a very challenging high-dimensional multiobjective optimization problem. Traditional appr…
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
Siya Zhu, Raymundo Arróyave, Doğuhan Sarıtürk
Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-…