AIMC Topic: 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...
Neural Networks, Computer Machine Learning Catalysis Solvents Monte Carlo Method 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 Catalysis Nickel
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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...
Artificial Intelligence DNA Catalysis Alkynes Amination
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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 ...
Artificial Intelligence Machine Learning Data Science Catalysis
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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...
Deep Learning Protein Binding Proteins Catalysis Binding Sites Protein Engineering Protein Structure, Secondary 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...
Neural Networks, Computer Machine Learning Catalysis
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.

Nature communications May 30, 2022
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such pur...
Neural Networks, Computer Adsorption Catalysis Metal-Organic Frameworks
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A nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit.

Nanoscale May 5, 2022
The DNA toehold mediated strand displacement reaction is one of the semi-synthetic biology technologies for next-generation computers. In this article, we present a framework for a novel nonlinear neural network based on an engineered biochemical cir...
Neural Networks, Computer Artificial Intelligence DNA Catalysis Synthetic Biology
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Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particles.

Journal of the American Chemical Society Mar 23, 2022
Homogeneous catalysis is typically considered "well-defined" from the standpoint of catalyst structure unambiguity. In contrast, heterogeneous nanocatalysis often falls into the realm of "poorly defined" systems. Supported catalysts are difficult to ...
Deep Learning Carbon Metal Nanoparticles Catalysis Palladium
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History Dependence in a Chemical Reaction Network Enables Dynamic Switching.

Small (Weinheim an der Bergstrasse, Germany) Mar 8, 2022
This work describes an enzymatic autocatalytic network capable of dynamic switching under out-of-equilibrium conditions. The network, wherein a molecular fuel (trypsinogen) and an inhibitor (soybean trypsin inhibitor) compete for a catalyst (trypsin)...
Neural Networks, Computer Catalysis Trypsin
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