jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Vahid | Masoud Sohrab Sohrab
Source: Analytical Methods in Environmental Chemistry Journal (article, 2020-03-01)
Field: Chemistry — Spectroscopy

Combination of reduction with metallic sodium and adsorption with mesoporous materials for re-refining of used insulating oil via experimental design In this study oils used direct influence sodium metallic on oxidation products and refining with the Mesoporous silicate material (MCM-41) and also silicate absorbents was synthesized and functionalized with aluminum salt through wet impreghation method (18wt%Al-MCM-41) (36wt% Al-MCM-41). Physical and structural properties has been investigated by FT-IR, BET,XRD and FESEM methods which shows a succful synthesis of the nano porous material. First step for reducing acid number oil we used direct reaction beetwen sodium metalic and used oil, after this step refining used oil by MCM-41 abosrbent has done. The effect of some parameters such as contact time, temperature and the dosage&nbsp; has been determined and optimized by respons surface method (RSM). The results show that acid number decrease obtained 150&nbsp;,60min and 200% of dosage. Optimum condation for absorbent reaction products and new oxidation products by MCM-41 obtained contact time of 120min temperature (120oC) and dosage 200% . Refining (metallurgy) ; Mesoporous material ; Materials science ; Sodium silicate ; Adsorption ; Chemical engineering ; Metal ; Porosity ; Response surface methodology ; Catalysis ; Nuclear chemistry ; Metallurgy ; Chemistry ; Composite material ; Chromatography ; Organic chemistry Aerogels and thermal insulation ; Enhanced Oil Recovery Techniques ; Mesoporous Materials and Catalysis
Authors: A.M.K. El-Ghonemy
Source: Bulletin of the Faculty of Engineering Mansoura University (article, 2020-07-12)
Field: Materials Science — Materials Chemistry

Gas Turbines waste heat/power recovery in tropical climate zones: Case study. (Dept. M ( Production ) ) The present study is applied on the gas turbines power plant that is currently under operation in northern kingdom of Saudi Arabia (KSA) since 1998. The plant consists of 7 gas turbine of 25MW each without inlet air cooling. More 2 gas turbine of 82MW each with inlet air cooling were added inDec 2007.In this area, during summer season, the ambient temperature may reach 50 °C, or higher in July and August. The objective is to present field results of this gas turbines power plant operation in order to measure and evaluate the performance. The results showed that, operating the plant at higher loads near to 100% is recommended for economic considerations, heat /power recovery by cooling the inlet air is advised, to overcome the problem of electricity black-out. Finally the results showed that disagreement between design and measured performance values is mainly due to higher ambient temperatures. DEPT ; Environmental science ; Production (economics) ; Waste management ; Tropical climate ; Geography ; Engineering ; Chemistry ; Archaeology ; Economics Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Offshore Engineering and Technologies
Authors: Jordan Berreur
Source: theses.fr (ABES) (preprint, 2020-11-24)
Field: Chemistry — Organic Chemistry

Accessing strong Lewis acids through Umpolung of chiral Lewis bases : applications in asymmetric catalysis Une méthodologie d’Umpolung de bases de Lewis chirales énantiopures permettant d’accéder à des cations fluorosulfoxonium et fluorophosphonium chiraux fortement acides de Lewis a été explorée. La base de Lewis choisie doit posséder au moins un élément de chiralité externe – chimiquement et configurationnellement stable dans les conditions réactionnelles appliquées – afin de garder un bon contrôle de la stéréochimie des acides préparés. Le potentiel en catalytise des composés acides de Lewis obtenus a été évaluée. Malgré de bonnes activités catalytiques, la possibilité d’induire de l’énantiosélectivité avec ces espèces en tant que catalyseurs reste encore à démontrer. Ces résultats nous ont conduit à questionner et étudier le rôle véritablement joué par ces acides de Lewis. Une stratégie de mono-fluoration électrophile directe de bases de Lewis très faibles a été mise au point. Cette stratégie surpasse la méthodologie décrite précédemment, puisqu’elle permet notamment d’accéder à des acides de Lewis très forts à partir de substrats parfois totalement inertes dans les conditions d’Umpolung classiques. L’acidité de Lewis des nouveaux cations fluorés ainsi préparés a pu être quantifiée par des méthodes expérimentales et ab initio. Umpolung ; Lewis acids and bases ; Catalysis ; Chemistry ; Frustrated Lewis pair ; Enantioselective synthesis ; Combinatorial chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis
Authors: Zuzana Drobná | Miroslav Stýblo | Araceli Hernández‐Zavala | Lenka Langrová | David J. Thomas | Blakely M. Adair | Jiřı́ Dědina | Milan Svoboda | Tomáš Matoušek
Source: UNC Libraries (article, 2020-11-09)
Field: Chemistry — Analytical Chemistry

Oxidation state specific generation of arsines from methylated arsenicals based on l-cysteine treatment in buffered media for speciation analysis by hydride generation-automated cryotrapping-gas chromatography-atomic absorption spectrometry with the multiatomizer An automated system for hydride generation - cryotrapping- gas chromatography - atomic absorption spectrometry with the multiatomizer is described. Arsines are preconcentrated and separated in a Chromosorb filled U-tube. An automated cryotrapping unit, employing nitrogen gas formed upon heating in the detection phase for the displacement of the cooling liquid nitrogen, has been developed. The conditions for separation of arsines in a Chromosorb filled U-tube have been optimized. A complete separation of signals from arsine, methylarsine, dimethylarsine, and trimethylarsine has been achieved within a 60 s reading window. The limits of detection for methylated arsenicals tested were 4 ng l−1. Selective hydride generation is applied for the oxidation state specific speciation analysis of inorganic and methylated arsenicals. The arsines are generated either exclusively from trivalent or from both tri- and pentavalent inorganic and methylated arsenicals depending on the presence of L-cysteine as a prereductant and/or reaction modifier. A TRIS buffer reaction medium is proposed to overcome narrow optimum concentration range observed for the L-cysteine modified reaction in HCl medium. The system provides uniform peak area sensitivity for all As species. Consequently, the calibration with a single form of As is possible. This method permits a high-throughput speciation analysis of metabolites of inorganic arsenic in relatively complex biological matrices such as cell culture systems without sample pretreatment, thus preserving the distribution of tri- and pentavalent species. Hydride ; Chemistry ; Genetic algorithm ; Cysteine ; Atomic absorption spectroscopy ; Analytical Chemistry (journal) ; Chromatography ; Hydrogen ; Organic chemistry ; Physics ; Biology Analytical chemistry methods development ; Dye analysis and toxicity ; Molecular Sensors and Ion Detection
Authors: Manel Mattoussi
Source: theses.fr (ABES) (article, 2020-07-24)
Field: Chemistry — Spectroscopy

Infrared spectroscopy of the molecule of CF4 Les travaux présentés dans ce manuscrit sont consacrés à l’étude de la spectroscopie FTIR à très haute résolution de la molécule de tétrafluorométhane CF4 en vue d’applications atmosphériques. Une très bonne modélisation de son spectre d’absorption est donc essentielle pour les mesures de concentration atmosphérique. Cette thèse a été effectuée dans le cadre du Groupe de Spectrométrie Moléculaire et Atmosphérique (GSMA) de l’Université de Reims Champagne Ardenne et au sein du Laboratoire de Spectroscopie et Dynamique Moléculaire (LSDM) de l’École Nationale Supérieure d’Ingénieurs de Tunis.Ce manuscrit est structuré en cinq parties. La première partie de ce travail de thèse concerne les généralités et les propriétés physiques spectroscopiques de CF4. La deuxième partie décrit les conditions expérimentales des différents spectres enregistrés soit à Reims ou à la ligne AILES du synchrotron SOLEIL. La troisième partie expose l’aspect théorique de la spectroscopie qui porte sur le formalisme tensoriel, Hamiltonien et moment dipolaire effectifs que nous avons utilisés pour calculer et traiter nos spectres. La quatrième partie offre une description des logiciels utilisés lors des analyses : MIRS et SpectrAssign. Finalement, dans la cinquième partie, nous présentons les résultats de l’analyse et nous les interprétons en donnant des comparaisons entre les spectres observés et calculés. Analytical Chemistry (journal) ; The Imaginary ; Molecular spectroscopy Spectroscopy and Laser Applications ; Advanced Physical and Chemical Molecular Interactions ; Molecular Spectroscopy and Structure
Authors: Tim Mueller
Source: ECS Meeting Abstracts (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

(Invited) Discovery and Design of Alloy Catalysts and Lithium-Ion Battery Materials through Machine Learning Drawing on examples from alloy catalysis and lithium-ion batteries, I will demonstrate how machine learning can be used to significantly accelerate the design and discovery of new materials. In the first example, I will present a way to create complete maps of the surface structures and catalytic properties of an alloy catalyst at every point in its phase diagram. The key to this approach is the generation of surface cluster expansions that depend on the lattice parameter of the bulk material and are trained using a Bayesian method. I will discuss how these maps reveal insights into the catalytic properties of intermetallic phases and demonstrate how the maps can be used to rapidly identify the target synthesis conditions for stable and active catalysts. In the second example, I will demonstrate how training an interatomic potential model using on-the-fly machine learning can accelerate the calculation of activation energies for lithium diffusion by seven orders of magnitude, resulting in significantly improved agreement with experiment compared to pure density functional theory. Using the machine-learned interatomic potential models, we have identified new candidate coating materials designed to improve interfacial stability in solid state lithium-ion batteries. Intermetallic ; Catalysis ; Materials science ; Density functional theory ; Alloy ; Battery (electricity) ; Phase diagram ; Lithium (medication) ; Ion ; Lattice (music) ; Computer science ; Thermodynamics ; Phase (matter) ; Computational chemistry ; Chemistry ; Physics ; Composite material Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Filipp Nikitin | Olexandr Isayev | Vadim Strijov
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions &lt;p&gt;Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space.&lt;/p&gt; Interpretability ; Computer science ; Graph ; Chemical space ; Artificial neural network ; Convolution (computer science) ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Filipp Nikitin | Olexandr Isayev | Vadim Strijov
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Our model uses an unsupervised approach to atom-mapping and bridges the gap between data-driven and traditional rule-based methods. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space. Interpretability ; Computer science ; Graph ; Chemical space ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Filipp Nikitin | Olexandr Isayev | Vadim Strijov
Source: ChemRxiv (preprint, 2020-07-17)
Field: Materials Science — Materials Chemistry

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict reaction outcome and atom-mapping during a chemical transformation. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space. Interpretability ; Computer science ; Graph ; Chemical space ; Theoretical computer science ; Convolution (computer science) ; Artificial neural network ; Template ; Artificial intelligence ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Vladimir B. Moskalev | Evgeniy O. Putin
Source: SHILAP Revista de lepidopterología (article, 2020-12-01)
Field: Chemistry — Physical and Theoretical Chemistry

PREDICTION OF REACTION CONDITIONS BY DEEP LEARNING TECHNIQUES Subject of Research. The paper presents a study of prediction method for various properties of reactions, such as the type of reaction, suitable groups of solvents, catalysts for the reaction. Molecular fingerprint differences between products and reagents were calculated using the RdKit chemical library as a representation of the reactions. Molecular fingerprints are widely used to predict various properties of molecules. Knowledge of the reaction conditions is essential for successful planning of retrosynthesis. Chemical informatics methods can effectively find the relationship between reaction reagents and the necessary conditions for the reaction. At this, the costs of time and resources spent on the determination of the necessary set of conditions for the reaction are reduced. Prediction of solvent groups can significantly improve the quality of models and the applicability of approaches. Method. LightGBM and a neural network with Deep Feature Selection were taken as machine learning models. The results were evaluated with the F1-metric. For the models training and evaluation, the data was broken down into chemically dissimilar parts. Bayesian optimization was used to optimize the searching of parameters. Main Results. Experiments were carried out to predict the reaction type, catalysts and solvent groups for the reaction. The obtained results show that the MLP type of reaction can be predicted equal to 0.99, a MLP catalyst equal to 0.7, and MLP group of solvents equal to 0.68 with F1-metric based on the difference in molecular fingerprints between reagents and products of machine learning models. Significant quantity of catalysts and solvents are considered in the paper. Practical Relevance. Automated planning of retrosynthesis is one of the topical areas of research. During planning, a sequence of necessary reactions is drawn up. The considered method can be used for recommendation system development that can suggest a possible group of catalysts and solvents to a chemical specialist, and, thus, reduce the cost of resources and time to determine the necessary reaction conditions. Artificial intelligence ; Computer science Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Shreeja Das | Hansraj Pegu | Kisor Sahu | Ameeya Kumar Nayak | Seeram Ramakrishna | Dibakar Datta | S. Swayamjyoti
Source: arXiv (Cornell University) (preprint, 2020-01-14)
Field: Materials Science — Materials Chemistry

Machine Learning in Materials Modeling -- Fundamentals and the Opportunities in 2D Materials The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials have been successfully developed to predict material properties for various applications such as materials for photovoltaic cells, thermoelectric materials, dielectrics, materials for batteries, fuel cells, etc. The setup of comprehensive materials databases, and openly accessible algorithm frameworks have also spurred the usage of machine learning for solving some of the most pressing problems in materials science. Some such recent implementations are discussed in this book chapter. A multitude of two-dimensional (2D) materials exist with the potential to replace the conventional materials for energy storage and nanodevices. The challenges faced in designing batteries and how machine learning tools can help in screening and narrowing down on the best composition, as well as the synthesis of air-stable 2D materials, are also discussed. Implementation ; Computer science ; Photovoltaic system ; Thermoelectric materials ; Nanotechnology ; Materials science ; Engineering ; Electrical engineering ; Software engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Fuel Cells and Related Materials
Authors: Ioan-Bogdan Magdău | Thomas F. Miller
Source: ChemRxiv (preprint, 2020-11-02)
Field: Materials Science — Polymers and Plastics

Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling Automated identification and classification of ion solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, fourdistinct Li+ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li+ solvation changes dramatically, featuring a rapid diversificationof solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation. Solvation ; Polymer ; Polymer electrolytes ; Solvent ; Swelling ; Propylene carbonate ; Ion ; Electrolyte ; Implicit solvation ; Materials science ; Chemical physics ; Chemistry ; Nanotechnology ; Organic chemistry ; Physical chemistry ; Ionic conductivity ; Composite material Conducting polymers and applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Yuqi Song | Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2020-12-16)
Field: Materials Science — Materials Chemistry

Computational discovery of new 2D materials using deep learning generative models Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to be challenging. Herein we propose a deep learning generative model for composition generation combined with random forest based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267,489 new potential 2D materials compositions and confirmed twelve 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery. Generative grammar ; Computer science ; Artificial intelligence ; Crystal structure prediction ; Stability (learning theory) ; Photovoltaics ; Chemical space ; Random forest ; Deep learning ; Machine learning ; Materials science ; Crystal structure ; Drug discovery ; Engineering ; Photovoltaic system ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications