AIMC Topic: 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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Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 1, 2022
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys t...
Machine Learning Catalysis Alloys
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Enzyme-Photocatalyst Tandem Microrobot Powered by Urea for Escherichia coli Biofilm Eradication.

Small (Weinheim an der Bergstrasse, Germany) Feb 5, 2022
Urinary-based infections affect millions of people worldwide. Such bacterial infections are mainly caused by Escherichia coli (E. coli) biofilm formation in the bladder and/or urinary catheters. Herein, the authors present a hybrid enzyme/photocataly...
Humans Biofilms Escherichia coli Robotics Catalysis Titanium Urease Urea
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Pd/silicalite-1: An highly active catalyst for the oxidative removal of toluene.

Journal of environmental sciences (China) Dec 29, 2021
Catalytic combustion is thought as an efficient and economic pathway to remove volatile organic compounds, and its critical issue is the development of high-performance catalytic materials. In this work, we used the in situ synthesis method to prepar...
Oxidative Stress Oxidation-Reduction Oxides Catalysis Toluene
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Tungsten oxide polymorphs and their multifunctional applications.

Advances in colloid and interface science Dec 29, 2021
Owing to the natural abundance, easy availability, high stability, non-stoichiometry, and chemical diversity, considerable interest has been devoted to tungsten oxide (WO) nanomaterials, and many advances have been achieved ranging from traditional c...
Artificial Intelligence Oxides Catalysis Tungsten
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Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization.

Bioresource technology Dec 7, 2021
Heterogeneously catalyzed lignin solvolysis opens the possibility of transforming low value biomass into high value, useful aromatic chemicals, however, its reaction behavior is poorly understood due to the many possible interactions between reaction...
Machine Learning Biomass Catalysis Lignin
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Atomic Structure-Free Representation of Active Motifs for Expedited Catalyst Discovery.

Journal of chemical information and modeling Aug 23, 2021
To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce t...
Machine Learning Catalysis Binding Sites
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