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Goldsmith, Bryan R.

Publications and source records attributed to Goldsmith, Bryan R..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-Performance Iridium–Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Optimization Discovery and Experimental Testing

Ir oxides are costly and scarce catalysts for oxygen evolution reaction (OER) in acid. There has been extensive interest in developing alternatives that are either Ir-free or require smaller amounts of Ir to drive the reactions at acceptable rates. One design strategy is to identify Ir-based mixed oxides that achieve similar performance while requiring smaller amounts of Ir. The obstacle to this strategy has been a very large phase space of the Ir-based mixed metal oxides, in terms of the metals combined with Ir and the different crystallographic structures of the mixed oxides, which prevents a thorough exploration of possible materials. In this work, we developed a workflow that uses machine-learning-aided Bayesian optimization in combination with density functional theory to make the exploration of this phase space plausible. This screening identified Mo as a promising dopant for forming acid-tolerant Ir-based oxides for the OER. We synthesized and characterized the Ir–Mo mixed oxides in the form of thin-film electrocatalysts with a known surface area. We show that these mixed oxides exhibited overpotentials ~30 mV lower than a pure Ir control while maintaining 24% lower Ir dissolution rates than the Ir control. Furthermore, these findings suggest that Mo is a promising dopant and highlight the promise of machine learning to guide the experimental exploration and optimization of catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metal Oxynitrides for the Electrocatalytic Reduction of Nitrogen to Ammonia

The successful deployment of technologies for the electrocatalytic nitrogen reduction reaction (e-NRR) to synthesize ammonia would enable distributed ammonia production with lower greenhouse gas emissions compared to the Haber–Bosch process. However, electrocatalysts that can readily activate N 2 , promote selective ammonia formation over the competing hydrogen evolution reaction, and maintain stability under reaction conditions are needed to enable this technology. Herein, we give our perspective on metal oxynitrides (A x B y O w N z ) as an emerging and underexplored materials class for e-NRR. Furthermore, we contrast the activity, selectivity, and stability of metal oxynitrides with those of their metal nitride and metal oxide counterparts. We discuss the different possible e-NRR reaction mechanisms on metal oxynitrides, emphasize challenges related to using metal oxynitrides for e-NRR, and provide an outlook for future research. Ultimately, the huge design space of metal oxynitrides is ripe for exploration to find catalyst formulations that overcome some of the limitations of traditional metal oxides and metal nitrides for e-NRR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Explaining the structure sensitivity of Pt and Rh for aqueous-phase hydrogenation of phenol

Phenol is an important model compound to understand the thermocatalytic (TCH) and electrocatalytic hydrogenation (ECH) of biomass to biofuels. Although Pt and Rh are among the most studied catalysts for aqueous-phase phenol hydrogenation, the reason why certain facets are active for ECH and TCH is not fully understood. Herein, we identify the active facet of Pt and Rh catalysts for aqueous-phase hydrogenation of phenol and explain the origin of the size-dependent activity trends of Pt and Rh nanoparticles. Phenol adsorption energies extracted on the active sites of Pt and Rh nanoparticles on carbon by fitting kinetic data show that the active sites adsorb phenol weakly. We predict that the turnover frequencies (TOFs) for the hydrogenation of phenol to cyclohexanone on Pt(111) and Rh(111) terraces are higher than those on (221) stepped facets based on density functional theory modeling and mean-field microkinetic simulations. The higher activities of the (111) terraces are due to lower activation energies and weaker phenol adsorption, preventing high coverages of phenol from inhibiting hydrogen adsorption. We measure that the TOF for ECH of phenol increases as the Rh nanoparticle diameter increases from 2 to 10 nm at 298 K and -0.1 V vs the reversible hydrogen electrode, qualitatively matching prior reports for Pt nanoparticles. The increase in experimental TOFs as Pt and Rh nanoparticle diameters increase is due to a larger fraction of terraces on larger particles. These findings clarify the structure sensitivity and active site of Pt and Rh for the hydrogenation of phenol and will inform the catalyst design for the hydrogenation of bio-oils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗