jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shangze Wu | Jonas Žurauskas | Michał Domański | Patrick S. Hitzfeld | Valeria Butera | Daniel J. Scott | Julia Rehbein | Ajeet Kumar | Erling Thyrhaug | Jürgen Hauer | Joshua P. Barham
Source: ChemRxiv (preprint, 2020-10-27)
Field: Chemistry — Organic Chemistry

Hole-Mediated PhotoRedox Catalysis: Tris(p-Substituted)biarylaminium Radical Cations as Tunable, Precomplexing and Potent Photooxidants &lt;p&gt;Electrochemically-mediated Photoredox Catalysis emerged as a powerful synthetic technique in recent years, overcoming fundamental limitations of electrochemistry and photoredox catalysis in the single electron transfer activation of small organic molecules. However, the mechanism of how photoexcited radical ion species with ultrashort (picosecond-order) lifetimes could ever undergo productive photochemistry has eluded synthetic chemists. We report tri(&lt;i&gt;para&lt;/i&gt;-substituted)biarylamines as a tunable class of electroactivated photocatalysts that become superoxidants in their photoexcited states, even able to oxidize molecules (such as dichlorobenzene and trifluorotoluene) beyond the solvent window limits of cyclic voltammetry. Furthermore, we demonstrate that precomplexation not only permits the excited state photochemistry of tris(&lt;i&gt;para&lt;/i&gt;-substituted)biarylaminium cations, but enables and rationalizes the surprising photochemistry of their &lt;i&gt;higher-order&lt;/i&gt; doublet (D&lt;i&gt;&lt;sub&gt;n&lt;/sub&gt;&lt;/i&gt;) excited states.&lt;/p&gt; Chemistry ; Photochemistry ; Photoredox catalysis ; Excited state ; Electron transfer ; Catalysis ; Radical ; Radical ion ; Molecule ; Tris ; Picosecond ; Electrochemistry ; Ion ; Photocatalysis ; Physical chemistry ; Organic chemistry ; Electrode Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Kobi Felton | Daniel Wigh | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2020-11-19)
Field: Materials Science — Materials Chemistry

Multi-task Bayesian Optimization of Chemical Reactions Recent work has shown how Bayesian optimization (BO) is an efficient method for optimizing expensive experiments such as chemical reactions. However, in previous studies, each optimization has been started from scratch with no information about previous or similar chemical optimization studies. Therefore, BO can still require more iterations than many experimental budgets provide. Here, we overcome this challenge using multi-task BO. Through in silico benchmarking studies, we show how past experimental data can be leveraged to improve the quality and speed of reaction optimization. Bayesian optimization ; Benchmarking ; Task (project management) ; Computer science ; Bayesian probability ; Scratch ; In silico ; Machine learning ; Artificial intelligence ; Chemistry ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Advanced Multi-Objective Optimization Algorithms
Authors: A Tkatchenko | J Smith | S Dick | M Fernandez-Serra | K Schtt | M Gastegger | K.-R Mller | R Maurer | Y Liu | P Kilby | T Frankcombe | T Schmidt | A Vaucher | F Zipoli | J Geluykens | V Nair | P Schwaller | T Laino | F Hse | L Roch | P Friederich | A Aspuru-Guzik
Source: Nature Communications (article, 2020-09-28)
Field: Materials Science — Materials Chemistry

Computation sparks chemical discovery Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field. Computer science ; Computation ; Data science ; Drug discovery ; Computational biology ; Bioinformatics ; Biology ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hany H. Abdel Ghafar | Emad K. Radwan | N.M. Khalil | Yousef Eljamal
Source: Egyptian Journal of Chemistry (article, 2020-02-18)
Field: Chemistry — Organic Chemistry

Removal of reactive yellow 160 from aqueous solution by alumina nanoparticles derived from aluminium waste residue In the current study, alumina nanoparticles were prepared from waste aluminum residue by an extraction and gel formation method. The prepared alumina nanoparticles were used to remove reactive yellow 160 (RY160) from an aqueous solution. The kinetics and adsorption equilibrium were discussed as well. The results show that, the heat-treated samples were consistings only of Al and O elements only and their crystalline phase was pure alumina in the nano scale. The alumina nanoparticles were effective adsorbents for the removal of RY160 from aqueous solution with percentage removal 100 % for samples heat-treated at 110°C and 600°C. The adsorption data was well fitted to Langmuir isotherm model for all prepared samples indicating a monolayer adsorption. The adsorption process was found to be endothermic and the adsorption takes place physically on the surface of alumina nanoparticles. The prepared alumina nanoparticles samples can be used as an efficient adsorbent in water treatment. Adsorption ; Endothermic process ; Aqueous solution ; Nanoparticle ; Langmuir adsorption model ; Monolayer ; Aluminium ; Materials science ; Chemical engineering ; Aqueous two-phase system ; Chemistry ; Nuclear chemistry ; Inorganic chemistry ; Organic chemistry ; Nanotechnology Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Coagulation and Flocculation Studies
Authors: Bo Zhang | Yunmin Liang | Biwei Liu | Wei Liu | Zhichun Liu
Source: Polymers (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

Enhancing the Thermo-Mechanical Property of Polymer by Weaving and Mixing High Length–Diameter Ratio Filler Improving thermo-mechanical characteristics of polymers can efficiently promote their applications in heat exchangers and thermal management. However, a feasible way to enhance the thermo-mechanical property of bulk polymers at low filler content still remains to be explored. Here, we propose mixing high length-diameter ratio filler such as carbon nanotube (CNT), boron nitride (BN) nanotube, and copper (Cu) nanowire, in the woven polymer matrix to meet the purpose. Through molecular dynamics (MD) simulation, the thermal properties of three woven polymers including woven polyethylene (PE), woven poly (p-phenylene) (PPP), and woven polyacetylene (PA) are investigated. Besides, using woven PE as a polymer matrix, three polymer nanocomposites, namely PE-CNT, PE-BN, and PE-Cu, are constructed by mixing CNT, BN nanotube, and Cu nanowire respectively, whose thermo-mechanical characteristics are compared via MD simulation. Morphology and phonons spectra analysis are conducted to reveal the underlying mechanisms. Furthermore, impacts of electron-phonon coupling and electrical field on the thermal conductivity of PE-Cu are uncovered via two temperature model MD simulation. Classical theoretical models are modified to predict the effects of filler and matrix on the thermal conductivity of polymer nanocomposites. This work can provide useful guidelines for designing thermally conductive bulk polymers and polymer nanocomposites. Materials science ; Composite material ; Nanocomposite ; Carbon nanotube ; Polymer ; Boron nitride ; Thermal conductivity ; Polymer nanocomposite ; Nanowire ; Nanotechnology Thermal properties of materials ; Carbon Nanotubes in Composites ; Advanced Thermoelectric Materials and Devices
Authors: Mack H. Cullison | Cheng Zhen | Jeshua J. Olson-Gross | Yi Xie | Michael Benson | Tianyi Chen
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2020-02-14)
Field: Materials Science — Materials Chemistry

Microstructure study of U–35 wt.% Zr alloy after quick annealing at 650 °C Abstract Microstructure ; Alloy ; Annealing (glass) ; Materials science ; Metallurgy Nuclear Materials and Properties ; Fusion materials and technologies ; High Temperature Alloys and Creep
Authors: Julia Schmidt | Joseph A. Weatherby | Isaac J. Sugden | Alejandro Santana‐Bonilla | Francesco Salerno | Matthew J. Fuchter | Erin R. Johnson | Jenny Nelson | Kim E. Jelfs
Source: ChemRxiv (preprint, 2020-06-11)
Field: Chemistry — Organic Chemistry

Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules &lt;p&gt;Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.&lt;/p&gt; Helicene ; Intermolecular force ; Molecule ; Organic semiconductor ; Chemical physics ; Electron mobility ; Materials science ; Rational design ; Charge (physics) ; Nanotechnology ; Side chain ; Electron transport chain ; Computational chemistry ; Chemistry ; Polymer ; Organic chemistry ; Physics ; Optoelectronics Synthesis and Properties of Aromatic Compounds ; Surface Chemistry and Catalysis ; Advanced Chemical Physics Studies
Authors: Felix Brosge | Johannes Florian Kochs | Mariela Bregu | Khai‐Nghi Truong | Kari Rissanen | Carsten Bolm
Source: Organic Letters (article, 2020-08-04)
Field: Chemistry — Organic Chemistry

5-Carbonyl-1,3-oxazine-2,4-diones from <i>N</i>-Cyanosulfoximines and Meldrum’s Acid Derivatives At elevated temperatures, <i>N</i>-cyanosulfoximines react with Meldrum's acid derivatives to give sulfoximines with <i>N</i>-bound 5-carbonyl-1,3-oxazine-2,4-dione groups. A representative product was characterized by single-crystal X-ray structure analysis. The product formation involves an unexpected molecular reorientation requiring several sequential bond-forming and -cleaving processes. Chemistry ; Meldrum's acid ; Molecule ; Medicinal chemistry ; Carbonyl group ; Product (mathematics) ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods ; Click Chemistry and Applications
Authors: B. U. Ugi
Source: Earthline Journal of Chemical Sciences (article, 2020-11-27)
Field: Materials Science — Materials Chemistry

Corrosion Inhibition of Cu-Zn-Fe Alloy in Hydrochloric Acid Medium by Crude Ethanol Extracts from Roots-Leaves Synergy of Solanum melongena The corrosion inhibition of Cu-Zn-Fe alloy in hydrochloric acid medium by crude ethanol extracts from roots-leaves synergy of Solanum melongena have been studied with chemical methods (mass loss and gasometric methods). At 3.0 grams per litre concentration of the roots-leaves synergy of Solanum melongena, it was observed that a 98.8 % inhibition efficiency was recorded as corrosion rate of alloy was decreasing with inhibitor increase. Temperature evaluation on the inhibitor showed 99.2 &gt; 88.4 &gt; 85.6 % as trial was conducted from 303-323 respectively, and in respect to increasing concentration, corrosion rate was found to be 1.718 &gt; 0.013, 0.0192 and 0.247 at 303, 313 and 323 respectively. All these present a good result for the synergistic inhibitor and a proof of its efficiency in controlling the corrosion of Cu-Zn-Fe alloy in hydrochloric acid medium. Inhibition mechanism was deduced from the activation and thermodynamic parameters that govern the process. Adsorption of extract on the Cu-Zn-Fe alloy was found to obey the Langmuir adsorption isotherm. The phenomenon of physical adsorption is proposed from the obtained thermodynamic parameters. Hydrochloric acid ; Corrosion ; Solanum ; Melongena ; Adsorption ; Alloy ; Ethanol ; Chemistry ; Langmuir adsorption model ; Nuclear chemistry ; Corrosion inhibitor ; Metallurgy ; Chromatography ; Materials science ; Inorganic chemistry ; Botany ; Biochemistry ; Organic chemistry ; Biology Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings ; Metal Extraction and Bioleaching
Authors: Arnold Lumsdaine | R. Maingi | Kevin G. Field | S.A. Gourlay | David Humphreys | Yutai Katoh | C. Kessel | Xiaorong Wang
Source: Fusion Engineering and Design (article, 2020-02-21)
Field: Materials Science — Materials Chemistry

Perspectives on the FESAC transformative enabling capabilities: Priorities, plans, and Status Transformative learning ; Advisory committee ; Fusion power ; Nuclear fuel cycle ; Computer science ; Coronavirus disease 2019 (COVID-19) ; Engineering management ; Fuel cycle ; Systems engineering ; Political science ; Nuclear engineering ; Plasma ; Medicine ; Engineering ; Sociology Fusion materials and technologies ; Superconducting Materials and Applications ; Nuclear Materials and Properties
Authors: Jochem H. Smit | Srinath Krishnamurthy | Bindu Y. Srinivasu | Rinky Parakra | Spyridoula Karamanou | Anastassios Economou
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-10-02)
Field: Chemistry — Spectroscopy

Probing universal protein dynamics using residue-level Gibbs free energy Abstract Hydrogen Deuterium Exchange Mass Spectrometry is a powerful monitor of protein intrinsic dynamics, yet the interpretation, visualization and cross-comparison of HDX-MS datasets is challenging. Here we present PyHDX, an open-source python package and web server, that batch-extracts the universal quantity Gibbs free energy at residue level over multiple protein conditions and homologues. Δ G values relate to protein normal modes and together provide a universal measure of protein flexibility. Availability PyHDX source code is released under the MIT license and can be accessed on GitHub . Python (programming language) ; Gibbs free energy ; Residue (chemistry) ; Mass spectrometry ; Computer science ; Visualization ; Chemistry ; Biological system ; Data mining ; Physics ; Chromatography ; Biochemistry ; Thermodynamics ; Biology ; Operating system Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Ivan Kopal | Júliana Vršková | Alžbeta Bakošová | Marta Harničárová | Ivan Labaj | Darina Ondrušová | Ján Valíček | Ján Krmela
Source: Polymers (article, 2020-11-11)
Field: Materials Science — Polymers and Plastics

Modelling the Stiffness-Temperature Dependence of Resin-Rubber Blends Cured by High-Energy Electron Beam Radiation Using Global Search Genetic Algorithm Modelling the influence of high-energy ionising radiation on the properties of materials with polymeric matrix using advanced artificial intelligence tools plays an important role in the research and development of new materials for various industrial applications. It also applies to effective modification of existing materials based on polymer matrices to achieve the desired properties. In the presented work, the effects of high-energy electron beam radiation with various doses on the dynamic mechanical properties of melamine resin, phenol-formaldehyde resin, and nitrile rubber blend have been studied over a wide temperature range. A new stiffness-temperature model based on Weibull statistics of the secondary bonds breaking during the relaxation transitions has been developed to quantitatively describe changes in the storage modulus with temperature and applied radiation dose until the onset of the temperature of the additional, thermally-induced polymerisation reactions. A global search real-coded genetic algorithm has been successfully applied to optimise the parameters of the developed model by minimising the sum-squared error. An excellent agreement between the modelled and experimental data has been found. Materials science ; Melamine resin ; Nitrile rubber ; Dynamic mechanical analysis ; Composite material ; Natural rubber ; Polymer ; Work (physics) ; Atmospheric temperature range ; Radiation ; Cathode ray ; Stiffness ; Electron ; Thermodynamics ; Physics Polymer Nanocomposite Synthesis and Irradiation ; Epoxy Resin Curing Processes ; Fiber-reinforced polymer composites
Authors: Jeffrey M. Ede
Source: Warwick Research Archive Portal (University of Warwick) (preprint, 2020-12-29)
Field: Materials Science — Materials Chemistry

Advances in Electron Microscopy with Deep Learning This doctoral thesis covers some of my advances in electron microscopy with deep learning. Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative adversarial networks for exit wavefunction reconstruction from single transmission electron micrographs. This thesis adds to my publications by presenting their relationships, reflections, and holistic conclusions. This version of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of Philosophy in Physics is typeset for physical printing and binding. Artificial intelligence ; Deep learning ; Computer science ; Artificial neural network ; Generative grammar ; Embedding ; Clipping (morphology) ; Pattern recognition (psychology) ; Machine learning Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Wesley F. Vaz | Lidiane J. Michelini | Gerlon de Almeida Ribeiro Oliveira | Luciano M. Lião | Caridad N. Pérez | Allen G. Oliver | Hamilton B. Napolitano
Source: Journal of Molecular Structure (article, 2020-06-01)
Field: Chemistry — Inorganic Chemistry

Structural comparison of five new halogenated dihydroquinoline-4(1H)-ones Chemistry ; Halogen ; Ring (chemistry) ; Halogen bond ; Stereochemistry ; Crystal structure ; Molecule ; Atom (system on chip) ; Crystallography ; Organic chemistry Crystal structures of chemical compounds ; Synthesis and biological activity ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Wilem Musu | Akihiro Tsuchida | Hirofumi Kawazumi | Nobuto Oka
Source: Joho Chishiki Gakkaishi (article, 2020-07-30)
Field: Chemistry — Analytical Chemistry

Quantitative Evaluation for the Peak Selectionin the Raman Spectroscopy Classification of Plastics Based on the Support Vector Machine To perform mechanical recycling of massive amounts of waste plastics, we developed an identifier oriented on industrial use and based on Raman spectroscopy. For the purpose of designing a practical classification technique that is accurate, fast, and robust against external noise, a simple comparison of spectroscopic peak intensities is considered to be more useful than a complicated multivariate analysis. However, such peak choosing is carried out empirically so far based on expert spectroscopic knowledges. In this report, we successfully demonstrate the classification of three kind plastics using only two peaks that are selected based on support vector machine (SVM) of machine learning; three types of plastic, polypropylene (PP), polystyrene (PS), and acrylonitrile‐butadiene‐styrene copolymer (ABS), are categorized well even in the case of the large artificially generated noise conditions. We could choose automatically a set of two peaks among six sets of four peaks that become candidates in the Raman spectra. Margin values in SVM allow obtaining the decisive information on how to select the peaks in the spectra quantitatively, for distinguishing different types of plastics. Support vector machine ; Polypropylene ; Polystyrene ; Raman spectroscopy ; Noise (video) ; Computer science ; Acrylonitrile butadiene styrene ; Materials science ; Set (abstract data type) ; Margin (machine learning) ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Analytical Chemistry (journal) ; Machine learning ; Composite material ; Chromatography ; Polymer ; Optics ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Maria F. Palafox | Valerie A. Arboleda | Keriann M. Backus
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-04)
Field: Chemistry — Spectroscopy

From Chemoproteomic-Detected Amino Acids to Genomic Coordinates: Insights into Precise Multi-omic Data Integration ABSTRACT The integration of proteomic, transcriptomic, and genetic-variant annotation data will improve our understanding genotype-phenotype associations. Due, in part, to challenges associated with accurate inter-database mapping, such multi-omic studies have not extended to chemoproteomics, a method that measure the intrinsic reactivity and potential ‘druggability’ of nucleophilic amino acid side chains. Here, we evaluated two mapping approaches to match chemoproteomic-detected cysteine and lysine residues with their genetic coordinates. Our analysis reveals that databases update cycles and reliance on stable identifiers can lead to pervasive misidentification of labeled residues. Enabled by this examination of mapping strategies, we then integrated our chemoproteomic data with in silico generated predictions of genetic variant pathogenicity, which revealed that codons of highly reactive cysteines are enriched for genetic variants that are predicted to be more deleterious. Our study provides a roadmap for more precise inter-database comparisons and points to untapped opportunities to improve the predictive power of pathogenicity scores and to advance prioritization of putative druggable sites through integration of predictions of pathogenicity with chemoproteomic datasets. Druggability ; Computational biology ; Data integration ; In silico ; Biology ; Pathogenicity ; Identification (biology) ; Genomics ; Computer science ; Genome ; Genetics ; Data mining ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics