As the artificial intelligence and machine learning revolution permeates every aspect of our lives, materials science and engineering is no exception. In fact, at the University of Florida, we are both developing and applying AI/ML tools for designing, fabricating, and characterizing advanced materials with optimized performance.
Whether by developing and implementing high throughput genetic algorithms, deep neural networks for structure-property relationship identification and analysis, or image segmentation and generative models for microscopy, we are leading the AI/ML revolution in materials.
Faculty
Ph.D., 2000, Washington University in St. Louis
Research interests: Machine Learning and AI, Computational Modeling and Simulation, Energy Materials, Electronic Materials, Engineering Education, Nanomaterials; 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.…