jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leyla Mohammadkhani | Majid M. Heravı
Source: Frontiers in Chemistry (review, 2020-11-16)
Field: Chemistry — Organic Chemistry

Microwave-Assisted Synthesis of Quinazolines and Quinazolinones: An Overview Microwave irradiation (MWI), as a unique, effective, sustainable, more economic, and greener source of energy compared to conventional heating, is applied in different organic transformations to result in the rapid formation of desired compounds due to thermal/kinetic effects. In this review, we try to underscore the applications of microwave irradiation (MWI) in the synthesis of quinazoline and quinazolinone derivatives that have been achieved and reported on in the last two decades. Quinazolinone ; Microwave irradiation ; Microwave ; Quinazoline ; Materials science ; Nanotechnology ; Combinatorial chemistry ; Chemistry ; Process engineering ; Computer science ; Telecommunications ; Engineering Quinazolinone synthesis and applications ; Microwave-Assisted Synthesis and Applications ; Multicomponent Synthesis of Heterocycles
Authors: Crispin Lichtenberg
Source: Chemistry - A European Journal (review, 2020-02-12)
Field: Chemistry — Organic Chemistry

Main‐Group Metal Complexes in Selective Bond Formations Through Radical Pathways Recent years have witnessed remarkable advances in radical reactions involving main-group metal complexes. This includes the isolation and detailed characterization of main-group metal radical compounds, but also the generation of highly reactive persistent or transient radical species. A rich arsenal of methods has been established that allows control over and exploitation of their unusual reactivity patterns. Thus, main-group metal compounds have entered the field of selective bond formations in controlled radical reactions. Transformations that used to be the domain of late transition-metal compounds have been realized, and unusual selectivities, high activities, as well as remarkable functional-group tolerances have been reported. Recent findings demonstrate the potential of main-group metal compounds to become standard tools of synthetic chemistry, catalysis, and materials science, when operating through radical pathways. Chemistry ; Group (periodic table) ; Reactivity (psychology) ; Radical ; Catalysis ; Metal ; Transition metal ; Radical cyclization ; Main group element ; Combinatorial chemistry ; Oxidative addition ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Organoboron and organosilicon chemistry ; Radical Photochemical Reactions
Authors: Thomas J. Hall | Victor M. Villapún | Owen Addison | Mark Webber | Morgan Lowther | Sophie Louth | Sophie E. Mountcastle | Mathieu Brunet | Sophie C. Cox
Source: Biomaterials Science (review, 2020-01-01)
Field: Chemistry — Organic Chemistry

A call for action to the biomaterial community to tackle antimicrobial resistance The global surge of antimicrobial resistance (AMR) is a major concern for public health and proving to be a key challenge in modern disease treatment, requiring action plans at all levels. Microorganisms regularly and rapidly acquire resistance to antibiotic treatments and new drugs are continuously required. However, the inherent cost and risk to develop such molecules has resulted in a drying of the pipeline with very few compounds currently in development. Over the last two decades, efforts have been made to tackle the main sources of AMR. Nevertheless, these require the involvement of large governmental bodies, further increasing the complexity of the problem. As a group with a long innovation history, the biomaterials community is perfectly situated to push forward novel antimicrobial technologies to combat AMR. Although this involvement has been felt, it is necessary to ensure that the field offers a united front with special focus in areas that will facilitate the development and implementation of such systems. This paper reviews state of the art biomaterials strategies striving to limit AMR. Promising broad-spectrum antimicrobials and device modifications are showcased through two case studies for different applications, namely topical and implantables, demonstrating the potential for a highly efficacious physical and chemical approach. Finally, a critical review on barriers and limitations of these methods has been developed to provide a list of short and long-term focus areas in order to ensure the full potential of the biomaterials community is directed to helping tackle the AMR pandemic. Call to action ; Antimicrobial ; Biomaterial ; Action (physics) ; Resistance (ecology) ; Chemistry ; Microbiology ; Biology ; Business ; Physics ; Ecology Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Bacterial biofilms and quorum sensing
Authors: Dmytro Strilets | Shixin Fa | Arthur Hardiagon | Marc Baaden | Tomoki Ogoshi | Mihail Bãrboiu
Source: Angewandte Chemie International Edition (article, 2020-09-27)
Field: Chemistry — Organic Chemistry

Biomimetic Approach for Highly Selective Artificial Water Channels Based on Tubular Pillar[5]arene Dimers Artificial water channels mimicking natural aquaporins (AQPs) can be used for selective and fast transport of water. Here, we quantify the transport performances of peralkyl-carboxylate-pillar[5]arenes dimers in bilayer membranes. They can transport ≈10<sup>7</sup> water molecules/channel/second, within one order of magnitude of the transport rates of AQPs, rejecting Na<sup>+</sup> and K<sup>+</sup> cations. The dimers have a tubular structure, superposing pillar[5]arene pores of 5 Å diameter with twisted carboxy-phenyl pores of 2.8 Å diameter. This biomimetic platform, with variable pore dimensions within the same structure, offers size restriction reminiscent of natural proteins. It allows water molecules to selectively transit and prevents bigger hydrated cations from passing through the 2.8 Å pore. Molecular simulations prove that dimeric or multimeric honeycomb aggregates are stable in the membrane and form water pathways through the bilayer. Over time, a significant shift of the upper vs. lower layer occurs initiating new unexpected water permeation events through toroidal pores. Bilayer ; Membrane ; Permeation ; Water transport ; Pillar ; Molecule ; Lipid bilayer ; Chemistry ; Aquaporin ; Chemical physics ; Chemical engineering ; Materials science ; Water flow ; Organic chemistry Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials
Authors: Xiaoran Roger Liu | Don L. Rempel | Michael L. Gross
Source: Nature Protocols (article, 2020-11-09)
Field: Chemistry — Spectroscopy

Protein higher-order-structure determination by fast photochemical oxidation of proteins and mass spectrometry analysis Footprinting ; Chemistry ; Mass spectrometry ; DNA footprinting ; Radical ; Photodissociation ; Protein folding ; Protein structure ; Combinatorial chemistry ; Biophysics ; Photochemistry ; Biochemistry ; DNA-binding protein ; Biology ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics
Authors: Gyoung S. Na | Seunghun Jang | Yea‐Lee Lee | Hyunju Chang
Source: The Journal of Physical Chemistry A (article, 2020-12-07)
Field: Materials Science — Materials Chemistry

Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations. Computer science ; Tuple ; Representation (politics) ; Exploit ; Artificial intelligence ; Machine learning ; Semantic gap ; Artificial neural network ; Perspective (graphical) ; Set (abstract data type) ; Graph ; Band gap ; Basis (linear algebra) ; Focus (optics) ; Theoretical computer science ; Mathematics ; Image (mathematics) ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Shuo Zhang | Yang Liu | Lei Xie
Source: arXiv (Cornell University) (preprint, 2020-11-15)
Field: Materials Science — Materials Chemistry

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. These models improve their expressive power by incorporating auxiliary information in molecules while inevitably increase their computational complexity. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. To achieve such goal, we propose a molecular mechanics-driven approach by first representing each molecule as a two-layer multiplex graph, where one layer contains only local connections that mainly capture the covalent interactions and another layer contains global connections that can simulate non-covalent interactions. Then for each layer, a corresponding message passing module is proposed to balance the trade-off of expression power and computational complexity. Based on these two modules, we build Multiplex Molecular Graph Neural Network (MXMNet). When validated by the QM9 dataset for small molecules and PDBBind dataset for large protein-ligand complexes, MXMNet achieves superior results to the existing state-of-the-art models under restricted resources. Molecular graph ; Computer science ; Multiplex ; Graph ; Theoretical computer science ; Artificial neural network ; Artificial intelligence ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wenshuo Liang | Guimin Lu | Jianguo Yu
Source: Advanced Theory and Simulations (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine‐Learning‐Based Deep Potential Abstract In previous work, molten magnesium chloride has been investigated using first‐principles molecular dynamics (FPMD) simulations based on density functional theory (DFT). However, such simulations are computationally intensive and therefore are restricted in terms of simulated size and time. In this work, a machine learning‐based deep potential (DP) is trained to accelerate the molecular dynamics simulation of molten magnesium chloride. The trained DP can accurately describe the energies and forces with the prediction errors in energy and force being 1.76 × 10 −3 eV/atom and 4.76 × 10 −2 eV Å −1 , respectively. Applying the deep potential molecular dynamics (DPMD) approach, simulations can be performed with more than 1000 atoms, which is infeasible for FPMD simulations. Additionally, the partial radial distribution functions, angle distribution functions, densities, and self‐diffusion coefficients predicted by DPMD simulations are also in reasonable agreement with FPMD or experimental results. This work shows that the DP enables higher efficiency and similar accuracy relative to DFT, exhibiting a bright application prospect in modeling molten salt systems. Molecular dynamics ; Work (physics) ; Magnesium ; Density functional theory ; Molten salt ; Diffusion ; Materials science ; Chloride ; Potential energy ; Atom (system on chip) ; Chemical physics ; Computational chemistry ; Chemistry ; Computer science ; Thermodynamics ; Atomic physics ; Physics ; Parallel computing ; Metallurgy Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Hydrogen Storage and Materials
Authors: Chaochao Yan | Qianggang Ding | Peilin Zhao | Shuangjia Zheng | Jinyu Yang | Yang Yu | Junzhou Huang
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist &lt;div&gt;Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.&lt;/div&gt;&lt;div&gt;However, most of them are cumbersome and lack interpretability about their predictions.&lt;/div&gt;&lt;div&gt;In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.&lt;/div&gt;&lt;div&gt;Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. &lt;/div&gt;&lt;div&gt;While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.&lt;/div&gt; Retrosynthetic analysis ; Synthon ; Interpretability ; Computer science ; Artificial intelligence ; Chemistry ; Stereochemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Jonas Köhler | Leon Klein | Frank Noé
Source: arXiv (Cornell University) (preprint, 2020-06-03)
Field: Materials Science — Materials Chemistry

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution of interest. Recent work showed that such generative models can be utilized in statistical mechanics to sample equilibrium states of many-body systems in physics and chemistry. To scale and generalize these results, it is essential that the natural symmetries in the probability density -- in physics defined by the invariances of the target potential -- are built into the flow. We provide a theoretical sufficient criterion showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design. Furthermore, we propose building blocks for flows which preserve symmetries which are usually found in physical/chemical many-body particle systems. Using benchmark systems motivated from molecular physics, we demonstrate that those symmetry preserving flows can provide better generalization capabilities and sampling efficiency. Homogeneous space ; Equivariant map ; Invariant (physics) ; Generalization ; Generative model ; Statistical physics ; Distribution (mathematics) ; Flow (mathematics) ; Probability distribution ; Mathematics ; Symmetry (geometry) ; Generative grammar ; Benchmark (surveying) ; Applied mathematics ; Computer science ; Pure mathematics ; Physics ; Artificial intelligence ; Mathematical analysis ; Geometry ; Mathematical physics ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Simon Kern | Sascha Liehr | Lukas Wander | Martin Bornemann‐Pfeiffer | Simon Müller | Michael Maiwald | Stefan Kowarik
Source: Analytical and Bioanalytical Chemistry (article, 2020-05-09)
Field: Chemistry — Analytical Chemistry

Artificial neural networks for quantitative online NMR spectroscopy Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. Graphical abstract. Artificial neural network ; Computer science ; Automation ; Process (computing) ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Process control ; Data mining ; Engineering ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Samad Hajinazar | A. Thorn | Ernesto D. Sandoval | Saba Kharabadze | Aleksey N. Kolmogorov
Source: Computer Physics Communications (article, 2020-10-18)
Field: Materials Science — Materials Chemistry

MAISE: Construction of neural network interatomic models and evolutionary structure optimization Python (programming language) ; Computer science ; Evolutionary algorithm ; Artificial neural network ; Computational science ; Workflow ; Fortran ; Interatomic potential ; Global optimization ; Source code ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Molecular dynamics ; Physics ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Georg Daniel Förster | Alice Castan | Annick Loiseau | Jaysen Nelayah | Damien Alloyeau | Frédéric Fossard | Christophe Bichara | Hakim Amara
Source: Carbon (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images High-resolution transmission electron microscopy ; Carbon nanotube ; Transmission electron microscopy ; Convolutional neural network ; Computer science ; Materials science ; Resolution (logic) ; Artificial intelligence ; Chirality (physics) ; Process (computing) ; Nanotechnology ; Pattern recognition (psychology) ; Physics Carbon Nanotubes in Composites ; Advanced Electron Microscopy Techniques and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Antonella Zaccara | Alice Petrucciani | Ismael Matino | Teresa Annunziata Branca | Stefano Dettori | Vincenzo Iannino | Valentina Colla | Michael Bampaou | K.D. Panopoulos
Source: Metals (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

Renewable Hydrogen Production Processes for the Off-Gas Valorization in Integrated Steelworks through Hydrogen Intensified Methane and Methanol Syntheses Within integrated steelmaking industries significant research efforts are devoted to the efficient use of resources and the reduction of CO2 emissions. Integrated steelworks consume a considerable quantity of raw materials and produce a high amount of by-products, such as off-gases, currently used for the internal production of heat, steam or electricity. These off-gases can be further valorized as feedstock for methane and methanol syntheses, but their hydrogen content is often inadequate to reach high conversions in synthesis processes. The addition of hydrogen is fundamental and a suitable hydrogen production process must be selected to obtain advantages in process economy and sustainability. This paper presents a comparative analysis of different hydrogen production processes from renewable energy, namely polymer electrolyte membrane electrolysis, solid oxide electrolyze cell electrolysis, and biomass gasification. Aspen Plus® V11-based models were developed, and simulations were conducted for sensitivity analyses to acquire useful information related to the process behavior. Advantages and disadvantages for each considered process were highlighted. In addition, the integration of the analyzed hydrogen production methods with methane and methanol syntheses is analyzed through further Aspen Plus®-based simulations. The pros and cons of the different hydrogen production options coupled with methane and methanol syntheses included in steelmaking industries are analyzed. Power to gas ; Hydrogen production ; Raw material ; Waste management ; Steam reforming ; Hydrogen ; High-temperature electrolysis ; Renewable energy ; Methane ; Methanol ; Environmental science ; Process integration ; Process engineering ; Electrolysis ; Chemistry ; Electrolyte ; Engineering ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Catalysts for Methane Reforming ; Hybrid Renewable Energy Systems
Authors: Qinyi Li | Qing Hao | Tianhui Zhu | Mona Zebarjadi
Source: Engineered Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Nanostructured and Heterostructured 2D Materials for Thermoelectrics The rapid development in the synthesis and device fabrication of two-dimensional (2D) materials provides new opportunities for their wide applications in a variety of fields including thermoelectric energy conversion, thermal management, and thermal logics. As one important research direction, the possibly poor thermoelectric performance of the pristine 2D materials can be dramatically improved with patterned nanostructures, and stacking of different 2Ds to form layered heterostructures, as well as electrostatic gating, which allow the fine-tuning of both the quasi Fermi level and phonon transport. This article reviews the recent advancement in this direction, with an emphasis on both fundamental understanding and practical problems. Thermoelectric materials ; Materials science ; Stacking ; Nanotechnology ; Heterojunction ; Thermoelectric effect ; Fabrication ; Engineering physics ; Thermal management of electronic devices and systems ; Phonon ; Nanostructure ; Energy transformation ; Thermal ; Thermal conductivity ; Optoelectronics ; Mechanical engineering ; Physics ; Condensed matter physics ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications