The Computational Materials Discovery Group develops physics-guided machine learning frameworks, inverse design, and multiscale modeling approaches to uncover fundamental material phenomena and accelerate the discovery of next-generation functional materials for energy and sustainability.
Our research spans organic semiconductors, halide perovskites, solid-state electrolytes, and plasmonic interfaces. We probe fundamental physics—including excited-state dynamics, charge transport, and electron–phonon coupling—to advance technologies such as blue OLED, perovskite and organic photovoltaics, solid-state batteries, and photocatalysis.
By coupling data-driven inverse design and machine-learned potentials with first-principles calculations, we bridge microscopic quantum mechanisms with macroscale functional properties. Our work is highly collaborative, uniting chemistry, physics, materials science, and computer science to validate predictions against experimental realization.
