Goldsmith Lab

RESEARCH GOALS AND HIGHLIGHTS

The Goldsmith Lab is dedicated to training the next generation of researchers to solve major societal challenges pertaining to energy and the environment. Our lab has expertise in first-principles computational modeling to understand catalysts and materials under realistic conditions. Using this knowledge, we aim to generate a platform for the design of new catalysts and materials for use in sustainable fuel production, energy storage, and pollution reduction. See the summaries below for more information about our current research thrusts and recent research highlights.

Catalysts and materials for a sustainable future

Click the poster for info about some of our research group thrusts, namely: (i) Understanding nanoclusters and single atom catalysts supported by metal oxides for sustainable chemical conversion; (ii) Amorphous materials for their use as catalysts and supports for alternative fuel production and pollution reduction; (iii) Electrocatalysis for fuel production, energy storage, and wastewater remediation; and (iv) Using machine learning to accelerate discovery of catalysts and materials.

Machine learning for heterogeneous catalysis

In a recent perspective, Goldsmith and colleagues discuss several examples where machine learning is making an impact on heterogeneous catalysis research such as: (1) accelerating catalyst active site determination and catalyst screening; (2) finding descriptors and patterns in catalysis data; (3) creating interatomic potentials for catalyst simulation; and (4) discovering and analyzing catalytic mechanisms. Ultimately, the power of machine learning has just begun to be exploited in heterogeneous catalysis research, with much room remaining for advancement. Read here.

Electrocatalytic reduction of wastewater pollutants

With the decreasing price of renewable electricity, electrocatalysis is becoming a promising approach to sustainably remediate pollutants in wastewater. One major wastewater pollutant is nitrate (NO₃⁻). Nitrate pollution of aquatic ecosystems and drinking water has become a serious problem around the world and poses a threat to human health and the environment. To help address this challenge, we are studying the reaction mechanism for nitrate reduction on a variety of electrocatalysts, with the goal to engender the design of improved nitrate reduction electrocatalysts. Our computational predictions are validated and refined through experimental collaborations. Read here and here.

Single atom and nanocluster catalysis

Single atoms and nanoclusters can show dramatically different activity, selectivities, and stabilities for a variety of important reactions, e.g., the reverse water gas shift reaction, C-H activation, and CO oxidation. In collaboration with General Motors and Tianjin University, we recently reported the use of isolated Pt₁ atoms on ceria as “seeds” to develop a Pt-O-Pt ensemble, which is well-represented by a Pt₈O₁₄ model cluster that retains 100% metal dispersion. The Pt atom in the ensemble is 100–1000 times more active than their single-atom Pt₁/CeO₂ parent in catalyzing the low-temperature CO oxidation under oxygen-rich conditions. Read article here.

Redox flow batteries for energy storage

Understanding the interfacial chemical physics and reactions on redox electrocatalysts will directly impact energy storage applications, enabling lower cost, higher energy/power density systems. A molecular level understanding of electrochemistry can be enabled by carefully combining controlled first-principles modeling with kinetic measurements and spectroscopy. We are working to understand the molecular structure of ions in aqeuous-phase redox flow batteries and their electrocatalytic redox mechanism. The Goldsmith lab leads the computational modeling efforts of this collaborative project. The below image shows a snapshot of a simulated [V(H₂O)₆]³⁺ complex using ab initio molecular dynamics. Read article here.

Data mining to discover materials insights

As part of the Novel Materials Discovery Laboratory, a major goal is to develop and exploit data analytics tools to uncover scientific insights from large materials datasets. Dr. Goldsmith's group is using exploratory datamining tools like subgroup discovery to find and describe interesting local patterns in materials data. While at the Fritz Haber Institute and in collaboration with Dr. Mario Boley, two illustrative examples were considered to: (1) discover interpretable models that classify the octet binary materials as either zincblende or rocksalt, and (2) elucidate structure-property relationships of gold clusters in the gas phase. Read here and here.