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Computational material discovery is under intense study owing to its abilityto explore the vast space of chemical systems. Neural network potentials (NNPs)have been shown to be particularly effective in conducting atomisticsimulations for such purposes. However, existing NNPs are generally designedfor narrow target materials, making them unsuitable for broader applications inmaterial discovery. To overcome this issue, we have developed a universal NNPcalled PreFerred Potential (PFP), which is able to handle any combination of 45elements. Particular emphasis is placed on the datasets, which include adiverse set of virtual structures used to attain the universality. Wedemonstrated the applicability of PFP in selected domains: lithium diffusion inLiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, anorder-disorder transition of Cu-Au alloys, and material discovery for aFischer-Tropsch catalyst. They showcase the power of PFP, and this technologyprovides a highly useful tool for material discovery.

Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements