Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
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We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of... ...
