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Accelerating complex materials discovery with universal machine-learning potential-driven structure prediction
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DOI：10.1016/j.mtener.2025.102059.png)
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Abstract
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• Universal machine-learning potentials enable the discovery of seven new stable quaternary oxide materials •M3GNet successfully rediscovers known materials absent from its training dataset •Structural search algorithms become the primary bottleneck due to computational costin crystal structure prediction
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