Artificial intelligence (AI) is transforming scientific discovery, particularly in materials science, by accelerating the prediction and design of materials with desired properties. Traditional physics-based modeling of atomic systems is computationally prohibitive for large-scale problems, and AI addresses this challenge by learning the underlying physics from data, thereby accelerating discoveries. In this talk I will present advances in AI-driven materials discovery, focusing on integrating physical principles, such as symmetry and equivariance, into AI models for accurate prediction and generation. My key approaches include geometric deep learning, language models, and AI agents, which collectively enhance the efficiency of materials discovery and strengthen the connection between AI and physical sciences. I will discuss my ongoing and future work, aiming at developing foundation models for materials and generic atomic systems, along with automated AI-driven discovery pipelines. I will conclude by presenting my future plans in tackling more complex and multiscale systems, spanning disordered materials and high-entropy alloys to general AI for science problems. Altogether, my current and future research demonstrates the close synergy among AI, physics, chemistry, and materials science.