Department of Materials Science and Engineering
National University of Singapore
Assistant Professor Deng Zeyu leads the Design and Engineering for Next-Gen Materials (DENG) Group, where he employs advanced computational techniques - including high-performance computing and machine learning - to accelerate the discovery of materials for clean energy applications. His research focuses on developing next-generation materials for energy storage, CO₂ capture, and optoelectronics. Notably, his recent work includes the development of kMCpy, a Python package designed to simulate transport properties in solids using kinetic Monte Carlo methods. Prof. Deng's interdisciplinary approach, combining materials science with data science, positions him at the forefront of computational materials research.
Department of Chemistry
National University of Singapore
NUS College of Humanities and Sciences
Assistant Professor Ou Pengfei is a recipient of the NUS Presidential Young Professorship. Prof. Ou leads the AI4Chem Group, where he pioneers the integration of artificial intelligence and machine learning in computational catalysis and electrochemistry. His research focuses on designing efficient catalysts for electrochemical reactions, utilizing advanced simulations and data-driven approaches to accelerate discovery processes. Recently, he has expanded the group's expertise into AI-assisted retrosynthetic planning, aiming to transform drug discovery and automated synthesis pathways. Prof. Ou's interdisciplinary expertise positions him at the forefront of merging computational chemistry with AI, making his insights invaluable to our discussions today.
Assistant Dean, Science, NUS Faculty of Science
Associate Professor, NUS Department of Physics
NUS College of Humanities and Sciences
Chairman, Science Research Programme Coordinating Committee
Prof Tan from the NUS Faculty of Science is today's moderator. A leading researcher in theoretical high-energy physics, Prof Tan is well-acquainted with the use of artificial intelligence in string theory, where machine learning algorithms can help determine the correct solution to the theory. This innovative approach has opened new avenues for solving the theory, which will ultimately lead to a full understanding of the formation of our universe. With both an engineering and physics background, Prof Tan's interdisciplinary expertise and commitment to advancing scientific frontiers make him an ideal guide for today's discussions.