Bryan R. Goldsmith

• PhD, University of California, Santa Barbara (2015), Chemical Engineering
• BS,  University of California, Riverside (2010), Chemical Engineering

Discovering improved 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 aids the process of designing systems because it provides a valuable way to test hypotheses and understand design criteria. The Goldsmith lab focuses on understanding catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy storage & generation. They use atomistic modeling (e.g., density-functional theory), molecular simulation, and data science / ML tools to extract key insights into catalysts and materials, with the goal of creating a platform for their design. Current lab research thrusts include (i) Thermo-, electro-, and plasma-assisted catalysis for air and water pollution reduction and sustainable chemical conversion; (ii) Machine learning to accelerate catalyst and materials design; and (iii) redox flow battery chemistry for renewable energy storage.

• NSF CAREER Award, 2023
• 1938E Award (University of Michigan College of Engineering), 2023
• ACS OpenEye Outstanding Junior Faculty Award, 2022
• Featured as a “Movers & Shakers” in Catalysis, The Catalyst Review magazine, 2022
• American Institute of Chemical Engineers 35 under 35 Award (Energy/Environment Category), 2020
• ACS Petroleum Research Fund Doctoral New Investigator, 2020
• Dow Corning Assistant Professorship in Chemical Engineering, 2020
• U.S. Delegate to the 67^th Lindau Nobel Laureate Meeting on Chemistry, 2017
• Alexander von Humboldt Postdoctoral Research Fellowship, 2016

Selected Recent Publications

Online full collection of our publications:

Google Scholar

Books

H. Scott Fogler, B. R. Goldsmith, E. Nikolla, N. Singh (2025). Elements of Chemical Reaction Engineering (7th ed.) Pearson Education. [link]

Journal Publications

• Bayesian optimization-guided design of silica-supported nickel catalysts from nickel phyllosilicates, T.-H. Wen, W.-L. Huang, P.-Y. Peng, Y.-R. Lu, C.-L. Chen, B. R. Goldsmith, Y.-C. Lin, ACS Engin. Au (2025). [doi]
• Enhancing the electrochemical detection of chloride using a machine learning approach, A. Schubert, A. Agrawal, K. Lee, S. Smith, B. R. Goldsmith, M. Burns, Sens. Actuators B: Chem. 443, 138193 (2025). [doi]
• Grand canonical study of the potential dependence of nitrate adsorption and dissociation across metals and dilute alloys, D. Sweeney, B. Tran and B. R. Goldsmith, Commun. Chem. 8 (2025).  [doi]
• Atomistic modeling of the active site formation mechanism and olefin metathesis kinetics for WO_x/SiO₂, O. Ohiro and B. R. Goldsmith, J. Phys. Chem. C. 129, 8121 (2025). [doi]
• High-performance iridium–molybdenum oxide electrocatalysts for water oxidation in acid: bayesian optimization discovery and experimental testing, J. Esterhuizen, A. Mathur, B. R. Goldsmith, S. Linic, J. Am. Chem. Soc. 146, 8 (2024). [doi]
• Substituent impact on quinoxaline performance and degradation in redox flow batteries, S. Modak, D. Pert, J. Tami, W. Shen, I. Abdullahi, X. Huan, A. McNeil, B. R. Goldsmith, D. G. Kwabi, J. Am. Chem. Soc. 146, 8 (2024). [doi]
• Theoretical investigation of the potential-dependent CO adsorption on copper electrodes, B. Tran and B. R. Goldsmith, J. Phys. Chem. Lett. 15, 25 (2024). [doi]
• Synergistic effects in organic mixtures for enhanced catalytic hydrogenation and hydrodeoxygenation, A. Mathanker, S. Halarnkar, B. Tran, N. Singh, B. R. Goldsmith, Chem. Catal. 101135 (2024). [doi]
• Electrocatalytic hydrogenation of phenol on platinum-cobalt alloys, J. Akinola, I. Barth, B. R. Goldsmith, N. Singh, J. Catal. 115331 (2024). [doi] [60th Anniversary Issue]
• Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification, C. Gruich, V. Madhavan, Y. Wang, B. R. Goldsmith, Mach. Learn.: Sci. Technol. 4, 025019 (2023). [doi]
• Effects of ions on electrocatalytic hydrogenation and oxidation of organics in aqueous phase, A. Mathanker, W. Yu, N. Singh, B. R. Goldsmith, Curr. Opin. Electrochem. 101347 (2023). [doi]
• Modeling plasma-induced surface charge effects on CO₂ activation by single atom catalysts supported on reducible and irreducible metal oxides, F. Doherty, B. R. Goldsmith, Plasma Sources Sci. Technol. 32, 034004 (2023). [doi] [Special Issue]
• Interpretable machine learning for knowledge generation in heterogeneous catalysis, J. A. Esterhuizen, B. R. Goldsmith, S. Linic, Nat. Catal. 5, 175 (2022). [doi]
• Metal oxynitrides for the electrocatalytic reduction of nitrogen to ammonia, S. Young, B. Ceballos, A. Banerjee, R. Mukundan, G. Pilania, B. R. Goldsmith, J. Phys. Chem. C. 126, 12980 (2022). [Editors’ Choice] [doi]
• Explaining the structure sensitivity of Pt and Rh for aqueous-phase hydrogenation of phenol, I. Barth, J. Akinola, J. Lee, O. Y. Gutiérrez, U. Sanyal, N. Singh, B. R. Goldsmith, J. Chem. Phys. 156, 104703 (2022). [Emerging Investigator Special Issue] [doi]
• Accelerating the structure search of catalysts with machine learning, E. Musa, F. Doherty, B. R. Goldsmith, Curr. Opin. Chem. Eng. 35, 100771 (2022). [Invited opinion article] [doi]
• Why halides enhance heterogeneous metal ion charge transfer reactions, J. Florian, H. Agarwal, N. Singh, B. R. Goldsmith, Chem. Sci. 12, 12704 (2021). [doi]
• Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning, J. A. Esterhuizen, B. R. Goldsmith, S. Linic, Chem Catalysis 1, 1 (2021). [doi]
• Perovskite oxynitrides as tunable materials for electrocatalytic nitrogen reduction to ammonia, S. D. Young, A. Banerjee, G. Pilania, B. R. Goldsmith, Trends in Chemistry 3, 694 (2021) [Invited Forum Article]. [doi]
• Rhodium single-atom catalysts on titania for reverse water gas shift reaction explored by first principles mechanistic analysis and compared to nanoclusters, F. Doherty, B. R. Goldsmith, ChemCatChem 13, 3155 (2021). [doi]
• Increasing electrocatalytic nitrate reduction activity by controlling adsorption through PtRu alloying, Z. Wang, S. D. Young, B. R. Goldsmith, N. Singh, J. Catal. 395, 143 (2021). [doi]
• Theory-guided machine learning finds geometric structure-property relationships for chemisorption on subsurface alloys, J. Esterhuizen, B. R. Goldsmith, S. Linic, Chem 6, 1 (2020). [doi]
• Nanocluster and single-atom catalysts for thermocatalytic conversion of CO and CO₂, F. Doherty, H. Wang, M. Yang, B. R. Goldsmith, Catal. Sci. Technol. 10, 5772 (2020). [doi]
• Adsorption energies of oxygenated aromatics and organics on rhodium and platinum in aqueous phase, J. Akinola, I. Barth, B. R. Goldsmith, N. Singh, ACS Catal. 10, 4929 (2020). [doi]
• Role of electrocatalysis in the remediation of water pollutants, N. Singh, B. R. Goldsmith, ACS Catal. 10, 3365 (2020). [Invited Viewpoint] [doi]
• Surpassing the single-atom catalytic activity limit through paired Pt-O-Pt ensemble built from isolated Pt₁ atoms, H. Wang, J.-X. Liu, W. Li, L. F. Allard, S. Lee, J. Wang, H. Li, J. Wang, X. Cao, M. Shen, B. R. Goldsmith, M. Yang, Nat. Commun. 10, 1 (2019). [doi]
• Understanding activity and selectivity trends in electrocatalytic nitrate reduction, J.-X. Liu, D. Richards, N. Singh, B. R. Goldsmith, ACS Catal. 9, 7052 (2019). [doi]
•  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. 5, eaav0693 (2019). [doi]