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Methodologies: Algorithms, High-Dimensional Data Analysis, Machine Learning, Optimization, Pattern Recognition, Predictive Models, Simulation, Statistical Modeling Connections: Novel Materials Discovery Laboratory (NOMAD)

Bryan R. Goldsmith

Assistant Professor, Chemical Engineering

Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. ¬†They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).