Education:

PhD Chemical Engineering, University of California Santa Barbara, 2015

BS Chemical Engineering, University of California Riverside, 2010

Teaching: ^top

ChE 230, Mass and Energy Balances (F17, F18)
ChE 696, Applied Data Science for Engineers (W19)

Research Interests: ^top

The development of novel catalysts and materials has never been more needed than today. The world is facing a growing population, mass consumerism, and rising greenhouse gas levels, all the while people strive to increase their standard of living. Computational modeling of catalysts and materials, and making use of its synergy with experiments, facilitates the process to design new systems since it provides a valuable way to test hypotheses and understand design criteria. Our research team focuses on obtaining a deep understanding of catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy storage & generation. We use first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., statistical learning and data mining) to extract key insights of catalysts and materials under realistic conditions, and to help create a platform for their design.

Professor Goldsmith’s research interests span topics such as heterogeneous catalysis, electrocatalysis, data science & machine learning, quantum mechanical modeling, and molecular simulation. The Goldsmith Research Group is involved in collaborations around the world, including Germany and China. Current research thrusts include: (i) Amorphous materials for their use as catalysts and supports for alternative fuel production and pollution reduction; (ii) Understanding nanoclusters and single atom catalysts supported by metal oxides for natural gas conversion; (iii) Electrocatalysis for fuel production, energy storage, and wastewater remediation; and (iv) Using machine learning to accelerate discovery of catalysts and materials.

Biography: ^top

Professional Experience

University of Michigan
Department of Chemical Engineering
Ann Arbor, Michigan

Assistant Professor, 2017-

Affiliations:

Michigan Catalysis Science and Technology Institute (CSTI)
Michigan Institute for Computational Discovery and Engineering (MICDE)
University of Michigan Energy Institute (UMEI)
Michigan Institute for Data Science (MIDAS)

Fritz Haber Institute of the Max Planck Society
Theory Department
Berlin, Germany

Humboldt Postdoctoral Research Fellow, 2015-2017

Awards: ^top

Young Scientist in the 67th Lindau Nobel Laureate Meeting in Chemistry, 2017

DAAD Travel Award, 2017

Alexander von Humboldt Postdoctoral Research Fellowship, 2016

Max Planck Society Postdoctoral Research Fellowship, 2015

UC Santa Barbara Co-Curricular Activities and Leadership Award, 2014

Schlinger Fellowship for Excellence in Chemical Engineering Research (one per year, UCSB), 2013

International Exchange in Materials Science Extended Research Travel Award, 2013

Amgen Scholar Alumni Travel Award, 2013

National Science Foundation PIRE-ECCI Graduate Research Fellowship, 2012

Academic Excellence Award in Chemical Engineering (one per year, UC Riverside), 2010

Bourns College of Engineering Scholarship, 2009

Tau Beta Pi Record Scholarship, 2009

Publications: ^top

Selected Publications
*corresponding author

New tolerance factor to predict the stability of perovskite oxides and halides, C. J. Bartel*, C. Sutton, B. R. Goldsmith, R. Ouyang, C. B. Musgrave, L. M. Ghiringhelli*, M. Scheffler, Sci. Adv. (2018) (Accepted).

Machine learning for heterogeneous catalyst design and discovery, B. R. Goldsmith*, J. Esterhuizen, C. J. Bartel, C. Sutton, J.-X. Liu, AIChE J. (2018). [doi]

Beyond ordered materials: understanding catalytic sites on amorphous solids, B. R. Goldsmith*, B. Peters, J. K. Johnson, B. C. Gates, S. L. Scott* ACS Catal. 7, 7543 (2017). (Perspective) [doi]

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery, M. Boley*, B. R. Goldsmith, L. M. Ghiringhelli, J. Vreeken, Data Min. Knowl. Disc. 31, 1 (2017) [doi]

Uncovering structure-property relationships of materials by subgroup discovery, B. R. Goldsmith*, M. Boley, J. Vreeken, M. Scheffler, L. M. Ghiringhelli*, New J. Phys. 19, 013031 (2017). [doi]

A Cu₂₅ nanocluster with partial Cu(0) character, T-A. D. Nguyen, Z. R. Jones, B. R. Goldsmith, W. R. Buratto, G. Wu, S. L. Scott*, T. W. Hayton*, J. Am. Chem. Soc. 137, 13319 (2015). [doi]

Rate-enhancing roles of water molecules in methyltrioxorhenium-catalyzed olefin epoxidation by H₂O₂, B. R. Goldsmith, T. Hwang, S. Seritan, B. Peters*, S. L. Scott*, J. Am. Chem. Soc. 137, 9604 (2015). [doi]

CO and NO-induced disintegration and redispersion of three-way catalysts rhodium, palladium and platinum: an ab initio thermodynamics study, B. R. Goldsmith, E. D. Sanderson, R. Ouyang, W.-X. Li*, J. Phys. Chem. C 118, 9588 (2014). [doi]

Isolated catalyst sites on amorphous supports: a systematic algorithm for understanding heterogeneities in structure and reactivity, B. R. Goldsmith, E. D. Sanderson, D. Bean, B. Peters*, J. Chem. Phys. 138, 204105 (2013). [doi]

B. R. Goldsmith, A. Fong, and B. Peters (2013). Understanding reactivity with reduced potential energy landscapes: recent advances and new directions. In K. Han & T. Chu, Reaction Rate Constant Computations: Theories and Applications (pp. 213-232). Royal Society of Chemistry. [doi]

Online collection of our publications:

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