  We introduce a computational method to optimize target physical properties in
the full configuration space regarding atomic composition, chemical
stoichiometry, and crystal structure. The approach combines the universal
potential of the crystal graph neural network and Bayesian optimization. The
proposed approach effectively obtains the crystal structure with the strongest
atomic cohesion from all possible crystals. Several new crystals with high
atomic cohesion are identified and confirmed by density functional theory for
thermodynamic and dynamic stability. Our method introduces a novel approach to
inverse materials design with additional functional properties for practical
applications.
