Research

Research Interest

Our group develops artificial intelligence and computational approaches to accelerate the discovery of molecules and materials. Our research focuses on integrating machine learning, data-driven methods, and physics-based simulations to understand structure–property relationships and enable the rational design of functional materials. By combining modern AI techniques with chemical and materials knowledge, we aim to efficiently explore vast chemical spaces and identify promising candidates for applications in energy, catalysis, and advanced materials.

In particular, our research spans machine learning and generative models for materials design, autonomous AI systems for scientific discovery, experimental data–driven modeling, and molecular simulation methods such as density functional theory, molecular dynamics, and Monte Carlo simulations. Through the integration of these approaches, MAPL seeks to build intelligent computational frameworks that bridge data, theory, and experimentation to accelerate next-generation materials discovery.