Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

doi:10.1038/s41524-026-01981-7

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

2026-02-04

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Sk Md Ahnaf Akif Alvi *

DOI：10.1038/s41524-026-01981-7

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The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

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