AIMC Topic: Catalysis

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Rational Design Strategies for Nanozymes.

ACS nano Jul 3, 2023
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...
Artificial Intelligence Peroxidases Peroxidase Nanostructures Catalysis
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A general model to predict small molecule substrates of enzymes based on machine and deep learning.

Nature communications May 15, 2023
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...
Catalysis Proteins Machine Learning Support Vector Machine Deep Learning
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Synthesis of δ-MnO via ozonation routine for low temperature formaldehyde removal.

Journal of environmental sciences (China) Apr 27, 2023
Nowadays, it is still a challenge to prepared high efficiency and low cost formaldehyde (HCHO) removal catalysts in order to tackle the long-living indoor air pollution. Herein, δ-MnO is successfully synthesized by a facile ozonation strategy, where ...
Air Pollution, Indoor Ozone Oxides Catalysis Oxidation-Reduction Formaldehyde Temperature Air Pollutants Manganese Compounds
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Computational modeling to assist in the discovery of supramolecular materials.

Annals of the New York Academy of Sciences Oct 17, 2022
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 ...
Artificial Intelligence Computer Simulation Humans Catalysis
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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 Oct 3, 2022
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...
Machine Learning Catalysis Neural Networks, Computer Monte Carlo Method Solvents Chemistry Techniques, Synthetic
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Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C-O Couplings.

Journal of the American Chemical Society Aug 8, 2022
Synthetic yield prediction using machine learning is intensively studied. Previous work has focused on two categories of data sets: high-throughput experimentation data, as an ideal case study, and data sets extracted from proprietary databases, whic...
Machine Learning Nickel Catalysis
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Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization.

Chemical reviews Jul 29, 2022
Reaction discovery and catalyst screening lie at the heart of synthetic organic chemistry. While there are efforts at catalyst design using computation/artificial intelligence, at its core, synthetic chemistry is an experimental science. This review...
Amination Artificial Intelligence Catalysis Alkynes DNA
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Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

Chemical reviews Jul 21, 2022
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding ...
Data Science Machine Learning Catalysis Artificial Intelligence
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Scaffolding protein functional sites using deep learning.

Science (New York, N.Y.) Jul 21, 2022
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without n...
Protein Binding Deep Learning Protein Structure, Secondary Protein Engineering Catalysis Proteins Binding Sites Protein Folding
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Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis.

Journal of chemical theory and computation Jun 23, 2022
Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computa...
Catalysis Machine Learning Neural Networks, Computer
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