Kang Group develops and employs advanced methodologies for structuring literature, experimental, and computational data, combined with artificial intelligence-driven correlation analysis. Our primary goal is to elucidate design principles, predict material properties, and develop novel materials for secondary batteries, catalytic systems, sensors, and artificial synapses. Our research integrates state-of-the-art machine learning algorithms—including deep learning and graph neural networks—with high-throughput screening and inverse design strategies. We leverage data-driven AI techniques to connect relationships across scales—from the nanoscale to the macroscale—with quantum mechanics-level atomic precision. This approach enables virtual experiments within simulation environments, accelerating the discovery and optimization of materials with improved performance and durability.

Open Positions