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
Education: Ph.D. 2014, Pennsylvania State University
Research Interest: Computational materials, CALPHAD, Phase-field method, Machine learning, Ferroelectric materials, Physical metallurgy, Recycling aluminum, Additive manufacturing, Microstructure evolution, Rapid solidification, Phase transformation
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.…
Education: Ph.D. 2019, University of Akron
Research Interest: Computational Soft Materials, Structure-Property Relation of Soft Materials, Polymer Physics, Bio-Inspired and Biomimetic Materials, Sustainable Polymers, Colloidal Suspensions.
Website: The Liang Lab
Education: Ph.D., 2008, University of California San Diego
Research Interests: Computational plasma physics, magnetic fusion, tokamak disruptions, scientific machine learning, plasma-material interaction.
Lab Website: McDevitt Lab
Education: Ph. D., 2002, The Pennsylvania State University
Research Interests: sustainable material synthesis; advanced functional ceramics; nuclear energy; applied and explainable AI in materials science, neuromorphic memory, connectomics and graph theory, single crystal growth, materials under extreme environments; radiation effects on electronics; plant-derived materials; bioceramics.
Lab Website: Nino Research Group (NRG)
Ph.D., 2008, University of Illinois, Urbana-Champaign
Research Interests: Computational materials science, Computational mechanics, Coevolution of microstructure and properties, Materials in Harsh Environments, Mesoscale modeling and simulation, Nuclear materials, Numerical methods
Lab Website: Tonks Research Group
Ph.D., 2010, Penn State University
Research Interests: Reactor Kinetics and Dynamics, Neutronics, Thermal Hydraulics, Multiphysics Simulation, Advanced Numerical Methods, Applied Mathematics, Advanced Code Coupling Techniques, Scientific Software Development, High Performance Computing
Lab Website: Florida Advanced Multiphysics Modeling and Simulation (FAMMoS) Lab