Related papers: Selectivity- and Activity-Aware Catalyst Descripto…
The low concentration of proton donors in alkaline HER, subsequently leading to the extra water adsorption and dissociation steps, identifies the value of active sites (edge and basal sites) and crystal phases in lowering the extra…
This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and…
Novel photoelectrocatalysts that use sunlight to power the CO$_2$ reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of…
In$_2$O$_3$-based catalysts have shown high activity and selectivity for CO$_2$ hydrogenation to methanol, however the origin of the high performance of In$_2$O$_3$ is still unclear. To elucidate the initial steps of CO$_2$ hydrogenation…
We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to…
It is a general challenge to design highly active or selective earth-abundant metals for catalytic hydrogenation. Here, we demonstrated an effective computational approach based on inverse molecular design theory to deterministically search…
In ceria-based catalysis, the shape of the catalyst particle, which determines the exposed crystal facets, profoundly affects its reactivity. The vibrational frequency of adsorbed carbon monoxide (CO) can be used as a sensitive probe to…
The catalytic sites of MoS$_{2}$ monolayers towards hydrogen evolution are well known to be vacancies and edge-like defects. However, it is still very challenging to control the position, size, and defective areas on the basal plane of…
The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus…
Tandem catalysis involves two or more catalysts arranged in proximity within a single reaction vessel, with the aim of synergistically aligning the catalysts' reaction pathways to maximize overall system performance. This study presents a…
The development of active catalysts for hydrogen evolution reaction (HER) made from low-cost materials constitutes a crucial challenge in the utilization of hydrogen energy. Earth-abundant molybdenum disulfide (MoS$_2$) has been discovered…
Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have…
Membrane-electrode-assembly (MEA) cells with copper (Cu) cathodes show strong potential for electrochemical CO2 reduction to ethylene (C2H4), but achieving high C2H4 selectivity remains a challenge due to competing hydrogen evolution. This…
Electrocatalytic CO2 reduction technology is key to mitigating greenhouse gas emissions and the energy crisis. However, controlling the selectivity of CO2RR products at low overpotential remains a challenge. In this paper, we predicted five…
Metal nanoparticle surfaces comprise of multiple planes with various atomic arrangements that interact with gases differently1,2. Identification of gas adsorption properties on all facets is an essential prerequisite for rational design of…
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques…
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, more than current model architectures and processing…
The electrocatalytic CO2 reduction reaction (CO2RR) is a complex multi-proton-electron transfer process that generates a vast network of reaction intermediates. Accurate prediction of free energy changes (G) of these intermediates and…
Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated…
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially…