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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

ExactBO: GPU-Accelerated Certified Bayesian Optimization for Continuous and Constrained Materials Design

Author(s)

Juan Esteban Florez-Coronel, Raymundo Arroyave, Douglas Allaire

On-Site Speaker (Planned)

Juan Esteban Florez-Coronel

Abstract Scope

Bayesian optimization (BO) can accelerate materials design, but its effectiveness depends on reliably maximizing the acquisition function at each iteration. Heuristic optimizers, such as multi-start gradient-based search, can miss strong Expected Improvement (EI) maximizers, while exhaustive grids become impractical in higher-dimensional design spaces. This work presents ExactBO, a certified acquisition-optimization framework that bounds EI over axis-aligned hyperboxes using interval arithmetic and refines the search domain through DIRECT-style partitioning. Hyperboxes whose EI upper bound cannot improve the incumbent solution within prescribed tolerances are discarded, enabling bounded global search rather than purely local acquisition maximization. The method is implemented with native MPI/CUDA kernels to distribute bound evaluations across one or more GPUs. ExactBO is benchmarked on 2D, 5D, and 10D problems and demonstrated for alloy design over continuous Co, Cr, Fe, Mn, Ni, V compositions and processing parameters, where partitioning and pruning naturally support compositional constraints.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, High-Entropy Alloys

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