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We introduce a computational method to optimize target physical properties inthe full configuration space regarding atomic composition, chemicalstoichiometry, and crystal structure. The approach combines the universalpotential of the crystal graph neural network and Bayesian optimization. Theproposed approach effectively obtains the crystal structure with the strongestatomic cohesion from all possible crystals. Several new crystals with highatomic cohesion are identified and confirmed by density functional theory forthermodynamic and dynamic stability. Our method introduces a novel approach toinverse materials design with additional functional properties for practicalapplications.

Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion