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