Richard Hennig
Professor, Alumni Professor of Materials Science & Engineering
Affiliations Computational Materials Science, Electronic Materials, Energy Materials, Faculty, Machine Learning / Artificial Intelligence, Materials Science and Engineering, , Modeling and Simulation, Nanomaterials
Materials Science & Engineering
Biography
Education: Ph.D., 2000, Washington University in St. Louis
Research interests: AI-driven and ab-initio materials science; deep learning and generative models for materials prediction and inverse design; interpretable machine-learning force fields; quantum and superconducting materials; molecular magnetic qubits and spin coherence; electronic-structure and phonon-based modeling of electron–phonon coupling; electrochemical and solid–liquid interfaces; and open computational frameworks linking predictive AI, first-principles simulation, and experiment.
Lab Website: Hennig Materials Theory Lab