  Realizing large materials models has emerged as a critical endeavor for
materials research in the new era of artificial intelligence, but how to
achieve this fantastic and challenging objective remains elusive. Here, we
propose a feasible pathway to address this paramount pursuit by developing
universal materials models of deep-learning density functional theory
Hamiltonian (DeepH), enabling computational modeling of the complicated
structure-property relationship of materials in general. By constructing a
large materials database and substantially improving the DeepH method, we
obtain a universal materials model of DeepH capable of handling diverse
elemental compositions and material structures, achieving remarkable accuracy
in predicting material properties. We further showcase a promising application
of fine-tuning universal materials models for enhancing specific materials
models. This work not only demonstrates the concept of DeepH's universal
materials model but also lays the groundwork for developing large materials
models, opening up significant opportunities for advancing artificial
intelligence-driven materials discovery.
