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 the design, fabrication, and characterization of 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. Examples of our current projects are presented below.
Faculty
Ph.D., 2000, Washington University in St. Louis
Research interests: Computational materials science, ab-initio methods, structure prediction algorithms, two-dimensional materials, materials for energy technologies, solid-liquid interfaces
Lab Website: Hennig Materials Theory Lab
Ph. D., 2002, The Pennsylvania State University
Research Interests: Multifunctional ceramics; energy materials; dielectrics and ionic conductors in bulk and thin film; single crystal growth; nuclear materials and detectors; 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