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As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. In this context, computational and machine learning driven…
For decades of catalysis research, the d-band center theory that correlates the d-band center and the adsorbate binding energy has successfully enabled the accelerated discovery of novel catalyst materials. Recent studies indicate that, on…
Metal-support interactions are frequently invoked to explain the enhanced catalytic activity of metal nanoparticles dispersed over reducible metal-oxide supports, yet the atomic scale mechanisms are rarely known. Here, we use scanning…
The rational design of bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is essential for achieving efficient and cost-effective overall water splitting. Atomically dispersed…
Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However,…
An external electric field (EEF) can impact a broad range of catalytic processes beyond redox systems. Computational design of catalysts under EEFs targeting specific operation conditions essentially requires accurate predictions of the…
Chemical looping methane partial oxidation provides an energy and cost effective route for methane utilization. However, there is considerable CO2 co-production in state-of-the-art chemical looping systems, rendering a decreased…
The shape-controlled synthesis of cuprous oxide (Cu$_2$O) photocatalysts with both low or high index crystal planes has received increasing attention due to their unique facet-dependent properties. Since they are cheap and earth abundant,…
Electrocatalytic hydrogen evolution reaction (HER) in alkaline media is a promising electrochemical energy conversion strategy. Ruthenium (Ru) is an efficient catalyst with a desirable cost for HER, however, the sluggish H2O dissociation…
Electrochemical CO2 reduction is a promising strategy for utilization of CO2 and intermittent excess electricity. Cu is the only single-metal catalyst that can electrochemically convert CO2 to multi-carbon products. However, Cu has an…
Na-ion solid-state electrolytes (Na-SSE) exhibit high potential for electrical energy storage owing to their high energy densities and low manufacturing cost. However, their mechanical properties critical to maintain structural stability at…
Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the…
To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuclear octahedral transition metal complexes deposited in an…
Designing electrocatalysts for HER in alkaline conditions to overcome the sluggish kinetics associated with the additional water dissociation step is a recognized challenge in promoting the hydrogen economy. To this end, delicately tuning…
Alloyed metal nanoparticles are a promising platform for plasmonically enabled hot-carrier generation, which can be used to drive photochemical reactions. Although the non-plasmonic component in these systems has been investigated for its…
Cu-based electrocatalysts exhibits enormous potential for electrochemical CO2 conversion to added-value products. However, high selectivity, specially towards C2+ products, remains a critical challenge for its implementation in commercial…
Efficient and selective CO2 electroreduction into value-added chemicals and fuels emerged as a significant approach for CO2 conversion, however, it relies on catalysts with controllable product selectivity and reaction paths. In this work,…
In this study, we introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored…
A long-standing puzzle for methanol electrooxidation is how to achieve a CO-free pathway and accurately understand the origin of electrocatalytic selectivity. Herein, we unequivocally demonstrate that the Bi-modified Pt/C follows a CO-free…
Porous monoliths are promising catalyst supports due to their high surface area, interconnected channels, thermal stability and mechanical robustness. However, their tunable topology complicates design: trade-offs between conversion and…