CSE Faculty Candidate Seminar - Keqiang Yan

Name: Keqiang Yan, Ph.D. Candidate from Texas A&M University

Date: Thursday, March 27, 2025 at 11:00 am

Location: Coda Building, Room 230 (Google Maps link)

Link: The recording of this in-person seminar will be uploaded to CSE's MediaSpace

Coffee and snacks provided!

Title: AI for Materials Discovery: Graphs, Language Models, and Agents

Abstract: 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.

Bio: Keqiang Yan is a Ph.D. candidate in Computer Science at Texas A&M University, working on scientific machine learning and AI for science. His work spans geometric deep learning and large language models for applications in materials science, protein science, and drug discovery. His work has been published in premier venues including NeurIPS, ICML, ICLR, Science Advances, and JMLR, and has been recognized with a D.E. Shaw Research Doctoral & Postdoctoral Fellowship. In addition to publishing his work, Keqiang Yan has contributed to open-source projects, including DIG and AIRS with over 2.4K stars on GitHub, and achieved top performance in open challenges, including the Open Catalyst Challenge (#3) and the Matbench formation energy (#1). Keqiang Yan has been a program committee member of ICML, ICLR, and NeurIPS regularly.