Abstract: First-principles methods have become indispensable research tools in modern physics, chemistry, materials science and other fields. Based on the fundamental principles of quantum mechanics, first-principles calculations can achieve highly accurate material property predictions by solving the complicated problem of interacting electrons. However, their widespread applications are limited by the high computational cost, posing significant challenges for large-scale materials simulations and the construction of materials big data. In recent years, the emergence of groundbreaking works, such as AlphaGo, AlphaFold, and ChatGPT, heralds the advent of a new era of artificial intelligence (AI), bringing transformative opportunities to first-principles calculations. Deep learning provides a novel research paradigm for the field, enabling us to overcome the bottlenecks of traditional methods through precise modeling and efficient prediction. In this article, we introduce a series of deep-learning based first-principles computation methods. By leveraging the deep-learning modeling of a fundamental quantity in density functional theory (DFT), the DFT Hamiltonian, we propose a neural network framework designed to satisfy the key prior knowledge of the Hamiltonian, including the principles of nearsightedness and equivariance. This method has been successfully applied to large-scale simulations of twisted van der Waals materials, as well as the construction of universal models based on materials big data. These advancements offer new opportunities for developing large materials models and advancing AI-driven materials discovery.