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The Goldsmith Lab First-Principles Modeling of Catalysts and Materials University of Michigan – Ann Arbor, Department of Chemical Engineering The Goldsmith Lab Methods B: Data science and machine learning for materials Pollution abatment Methods A: First-principles modeling and molecular simulation techniques Theme 1: Catalysis for Air and Water Pollution Reduction Theme 2: Machine Learning to Accelerate Catalyst Design Interpretable Machine Learning for Catalysis Theme 3: Catalysis for Renewable Energy Generation, Use, and Storage Density functional theory Minima and saddle search algorithms Slab models Ab initio thermodynamics and rate theories [1]. J. A. Esterhuizen, B.R. Goldsmith, S. Linic. Chem (2020). [2]. H. Wang et al. Nature Comm. 10 (2019). ➢ The objective of this research is to develop physically transparent and accurate structure- property models for understanding catalysts. Rh 3 S 4 (100) slab model Cutting-edge computational techniques yield an accurate description of catalyst and material electronic and geometric properties under realistic conditions [1]. J. P. Perdew and K. Schmidt, AIP Conf. Proc., 577 (2001); [2]. M. K. Sabbe et al., Catal. Sci. Tech. 2 (2012); [3]. B. R. Goldsmith, J. Florian et al., Phys. Rev. Mater. 3 (2019); [4]. K. Reuter and M. Scheffler, Phys. Rev. B 65 (2002); [5]. K. Reuter et al., Phys. Rev. Lett. 93 (2004); [6]. B. Peters, J. Phys. Chem. B 119 (2015). [1-2] Rh(CO) 2 / TiO 2 (101) Model the system at realistic chemical potentials Reaction rates 200 K Ρ = 68% 26% 1% 6% [3] [5] [6] Ab initio molecular dynamics Combining first-principles (electronic-structure theory) modeling and data science to understand catalysts and materials Goldsmith Lab (Fall 2020) • 5 PhD students • 2 MS students • 4 Undergraduate students Metal and Bimetallic Catalysts for Bio-Oil Hydrogenation We recently reported the use of isolated Pt 1 atoms on ceria as “seeds” to develop a Pt oxide nanocluster for CO oxidation, which is well-represented by a Pt 8 O 14 model cluster that retains 100% metal dispersion. The Pt atom in the nanocluster is 100–1000 times more active than their single-atom Pt 1 /CeO 2 parent in catalyzing the low- temperature CO oxidation under oxygen- rich conditions. [2] We plan to accelerate these methods more with ML. [1]. J.-X. Liu, D. Richards, N. Singh, and B. R. Goldsmith. ACS Catal. (2019). [2]. N. Singh and B. R. Goldsmith. ACS Catal. (2020). Data analytics applied to catalyst data offers opportunities to advance discovery We develop and apply machine learning approaches to uncover catalytic insights Explainable Boosting Machines ➢ Find interpretable global models of a target property in catalyst data EBM model predictions easily visualizable through ‘feature shapes’ Active learning/high-throughput Machine learning applied to alloys yields insights into the effect of the number of d-electrons in the ligand metal for various adsorbates. [1] Pt Surface Ligand Exploring alloy-induced ligand and strain effects on adsorption properties of alloys. In collaboration with Prof. Suljo Linic’s lab. [1] Strain effect Ligand effect Single Atom and Nanocluster Catalysis for CO 2 reduction ➢ CO 2 can be reduced via H 2 to produce either methane (CH 4 ) or carbon monoxide (CO), depending on the presence of nanoclusters or single atoms. ➢ What impact on catalytic activity and selectivity can be seen from varying the metal nanocluster size and support surface? ➢ Aqueous nitrate (NO 3 – ), a major water pollutant, can be remediated with electrocatalytic nitrate reduction. ➢ Metal alloys can perform this reaction with higher activity (higher turnover frequency, TOF) than their pure-metal counterparts. ➢ What alloy compositions make the most active and selective catalyst? ➢ In collaboration with Prof. Nirala Singh’s lab (UofM ChE). Redox Flow Batteries for Large-Scale Energy Storage ➢ Redox flow batteries are used to match power grid supply to demand, which is increasingly relevant as we transition to intermittent renewable energy sources. Pt x Ru y alloys for electrocatalytic nitrate reduction ● = Pt, ● = Ru ➢ Use machine learning (ML) to rapidly and accurately predict catalyst figures of merit. ➢ Strategically train ML model reduce number of costly DFT calculations. [3]. Akinola, J.; Barth, I.; Goldsmith, B. R.; Singh, N. ACS Catal. (2020). ➢ Biomass-derived molecules can be upgraded to fuels and industrially relevant chemicals using aqueous-phase electrocatalytic hydrogenation driven with renewable electricity Phenol Cyclohexanol [2] [2] Settles, B. Active Learning. (Morgan & Claypool Publishers, 2012), p. 8. Active learning strategy enables rapid screening of catalyst descriptors, such as adsorption energies [3] [4] [3] [1] ➢ We recently reported an anion bridging mechanism for the V 2+ /V 3+ redox reaction on glassy carbon electrodes for vanadium redox flow batteries. [1] ➢ We elucidated the structures and free energies of Ce 3+ and Ce 4+ in various electrolytes. [2] [1]. Agarwal, H.; Florian, J.; Goldsmith, B. R.; Singh, N. ACS Energy Lett. (2019). Change in adsorption energy Change in ligand d-electrons relative to host metal [2]. C. Buchannan et al. Inorg Chem. (2020). [1] B. R. Goldsmith et al. AIChE J. (2018). [1] Machine Learning Enabled High-Throughput Evaluation of Catalysts
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