Chuanyu Liu

Research Interests

Current AI-driven materials discovery often faces a bottleneck: models frequently over-idealize structures by predicting perfectly ordered crystallographic sites, neglecting the thermodynamically driven disorder inherent in bulk and surface structures under real-world conditions.

My methodological focus lies in developing physics-informed, data-driven machine learning methods to capture the fundamental laws of materials thermodynamics and surface kinetics from atomistic simulations and characterization data, bridging material behavior from the single-atom level up to experimental observables.

My goal is to close the loop between idealized computational design, the experimental workbench, and practical applications in catalysts, electrolytes, and energy storage materials, accelerating innovation at the AI-energy nexus.