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QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys
Machine-learned forward models can rapidly predict alloy properties, but their use for inverse design remains challenging when the search should also retain compatibility with quadratic unconstrained binary optimization (QUBO). Here, we develop a QUBO-compatible active-learning framework for inverse
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