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Ronald Pindak’s research is in condensed matter physics with an emphasis on the use of x-ray scattering techniques to characterize bulk, surface, and interface structures as well as their kinetics and dynamical fluctuations. Pindak worked for 24 years at Bell Laboratories where he achieved the rank of Distinguished Member of the Technical Staff. He has 45 years of research experience with over 100 refereed publications covering work on both soft condensed matter (complex fluids, colloids, polymers) and hard condensed matter thin films such as found in electronic and opto-electronic devices. He has 36 years of experience using synchrotron research facilities and 17 years of experience managing synchrotron facility operations. He currently oversees a suite of state-of-the-art beamlines at NSLS-II that are optimized for coherent, micro-beam, inelastic, resonant, and small/wide angle x-ray scattering.
Dr. Washington currently serves on multiple committees both at SRNL and in the Aiken community. These include the Conduct of R&D safety council, Diversity Board of Directors for SRNS, and the former Board of Directors Chairman and current member for Habitat for Humanity. He is an also an Adjunct Professor at USC Aiken in the chemistry department.
Dr. Marius Stan is the Intelligent Materials Design Lead in the Argonne National Laboratory’s Applied Materials division. Stan is a computational physicist and chemist interested in complexity, non-equilibrium thermodynamics, heterogeneity, and materials design for energy and electronics applications. He uses artificial intelligence, machine learning, and multi-scale computer simulations to understand and predict properties and evolution of complex physical systems.
Stan came to Argonne and the University of Chicago in 2010, from Los Alamos National Laboratory. He is a Senior Fellow at the University of Chicago’s Computation Institute (CI) and a senior Fellow of the Northwestern-Argonne Institute for Science and Engineering (NAISE).
The goal of Stan’s research is to discover or design materials, structures, and device architectures for energy applications, such as nuclear energy and energy storage, and for the new generation computers. To that end, he develops theory-based (as opposite to empirical) mathematical models of thermodynamic and chemical properties of imperfect materials. The imperfection comes from defects or deviations from stoichiometry (e.g., in battery electrodes), from irradiation (e.g. in nuclear fuels), or doping (e.g. computer memory devices). Then Stan uses the models in computer simulations of coupled heat and chemical transport, micro(nano)-structure evolution, phase-stability, and phase transformations. To analyze large and complex experimental and computational data sets, Stan uses Bayesian analysis and machine learning methods based on regression and evolutionary (genetic) algorithms that can produce robust data screening and sampling. In parallel, Stan designs experiments to validate the models and simulations.