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Catalysis

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Computational modeling to assist in the discovery of supramolecular materials.

Annals of the New York Academy of Sciences
Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential ...

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Proceedings of the National Academy of Sciences of the United States of America
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemi...

Data-driven enzyme engineering to identify function-enhancing enzymes.

Protein engineering, design & selection : PEDS
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of p...

A general model to predict small molecule substrates of enzymes based on machine and deep learning.

Nature communications
For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficien...

Rational Design Strategies for Nanozymes.

ACS nano
Nanozymes constitute an emerging class of nanomaterials with enzyme-like characteristics. Over the past 15 years, more than 1200 nanozymes have been developed, and they have demonstrated promising potentials in broad applications. With the diversific...

Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces.

Journal of chemical information and modeling
In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which, in many cases, limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based appr...

Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts.

Journal of chemical information and modeling
The design of new heterogeneous catalysts that convert small molecules into valuable chemicals is a key challenge for constructing sustainable energy systems. Density functional theory (DFT)-based design frameworks based on the understanding of molec...

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

Briefings in bioinformatics
The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of exp...

Optimization of catalytic wet air oxidation process in microchannel reactor for TBBS wastewater treatment.

Environmental technology
Catalytic wet air oxidation (CWAO) process is employed for the treatment of N-tert-butyl-2-benzothiazolesulfenamide (TBBS) wastewater in a microchannel reactor that enables continuous operation of the reaction and allows for thorough mixing of oxygen...

Invariant Molecular Representations for Heterogeneous Catalysis.

Journal of chemical information and modeling
Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory ...