Postdoctoral Researcher at Texas A&M University in the Arroyave Lab.
I work on data-driven, closed-loop computational alloy design with a focus on:
- Refractory high-temperature alloys
- Alloy additive manufacturing (3D printing)
- Bayesian optimization
- Machine learning for materials discovery
My work combines thermodynamics, optimization, and machine learning to accelerate alloy discovery workflows.
Current interests include:
- Efficient search of composition/processing spaces
- Physics-informed objectives for inverse design
- Active learning strategies for sparse, expensive evaluations
- Integration of CALPHAD and Bayesian optimization
- Texas A&M MSEN 210: Thermodynamics of Materials
- Texas A&M MSEN 655: Materials Design Studio
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Residual Driving Force Minimization for Single-Phase Alloy Discovery
Bayesian optimization framework for discovering single-phase regions efficiently in CALPHAD spaces. -
Closed-loop alloy design campaigns
End-to-end workflows that iteratively propose, evaluate, and refine candidates under practical constraints.
Python · PyCalphad · Bayesian Optimization · Gaussian Processes · Data-driven materials design
- GitHub: @BrentVela