AIMC Topic: Metals

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Chemical reviews Jul 14, 2021
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a...
High-Throughput Screening Assays Coordination Complexes Transition Elements Machine Learning Metals
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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications Jun 17, 2021
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate b...
Binding Sites Algorithms Catalytic Domain Enzymes Metals Static Electricity Metalloproteins Models, Molecular Machine Learning Databases, Protein Catalysis
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Research on the process of small sample non-ferrous metal recognition and separation based on deep learning.

Waste management (New York, N.Y.) Mar 28, 2021
Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materi...
Algorithms Aluminum Recycling Deep Learning Metals
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A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA Jan 26, 2021
End-of-life vehicles (ELVs) provide a particularly potent source of supply for metals. Hence, the recycling and sorting techniques for ferrous and nonferrous metal scraps from ELVs significantly increase metal resource utilization. However, different...
Aluminum Recycling Deep Learning Metals Neural Networks, Computer
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Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Analytical chemistry Jan 5, 2021
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...
Machine Learning Metals Particle Size Spectrum Analysis Lasers Air Pollutants Particulate Matter
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Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion.

Physics in medicine and biology Sep 25, 2020
The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body ...
Humans Algorithms Multimodal Imaging Dental Implants Metals Positron-Emission Tomography Artifacts Tomography, X-Ray Computed Image Processing, Computer-Assisted Deep Learning Magnetic Resonance Imaging
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Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Sep 7, 2020
PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method...
Artifacts Radiotherapy, Intensity-Modulated Deep Learning Metals Radiotherapy Planning, Computer-Assisted Algorithms Tomography, X-Ray Computed Humans
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Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation.

Chemosphere Jul 29, 2020
Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW framework was proposed, which divided the factors affecting coagulation into three parts consisting ...
Hydrogen-Ion Concentration Aluminum Hydroxide Drinking Water Water Purification Metals Models, Theoretical Neural Networks, Computer
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Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks.

Physical chemistry chemical physics : PCCP Jun 1, 2020
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations - the cost of representation generation, risk of inh...
Bromine Carbon Oxygen Deep Learning Hydrogen Nitrogen Phosphorus Iodine Sulfur Chlorine Metals Fluorine
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Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method.

Journal of computer-aided molecular design Mar 17, 2020
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...
Proteins Metals Ligands Databases, Protein Protein Binding Machine Learning Quantum Theory Computational Biology Drug Design
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