Computational Materials Design & Discovery Lab

We combine experimental and computational methods to understand materials properties and reaction mechanisms at the atomic scale and develop new energy storage materials, including electrodes and solid electrolytes for rechargeable batteries. First-principles calculations and density functional theory (DFT) connect atomic and electronic structures with ion transport, electrochemical reactions and degradation. Synthesis, characterization and cell testing help us discover new materials and validate predictions. We also combine artificial intelligence (AI), machine learning (ML) and robotics in self-driving laboratories to accelerate battery materials development. These platforms collect large experimental datasets, search for promising materials, optimize compositions and synthesis conditions, and test predictions from AI and atomistic modeling through synthesis and measurements. Keywords: lithium-ion batteries, electrodes, solid electrolytes, first-principles calculations, density functional theory (DFT), artificial intelligence, machine learning, self-driving lab. Discovering new electrode and electrolyte materials and understanding how local structure, synthesis pathways and reaction mechanisms govern ion transport, energy storage and degradation. We use first-principles calculations and atomistic simulations to understand material stability, electronic structure, reaction mechanisms and ion transport. Machine-learned interatomic potentials extend simulations to larger systems and longer timescales, while machine learning supports materials discovery, property prediction and battery diagnostics. Predictions are tested against experiments to guide materials design. We develop autonomous, self-driving laboratories to build large-scale experimental datasets for AI-driven