7 papers
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…
Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling
Siya Zhu, Raymundo Arroyave
Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational…
Predicting interstitial elements in Refractory Complex Concentrated Alloys
Aomin Huang, Siya Zhu, Calvin Belcher +4
Refractory complex concentrated alloys, composed of multiple principal refractory elements, are promising candidates for high-temperature structural applications due to their excep…
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…
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
Keqiang Yan, Montgomery Bohde, Andrii Kryvenko +10
Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLI…