jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Jahidul Islam | Md. Nuruzzaman Sarker | Ajoy Kumer | Sunanda Paul
Source: Cumhuriyet Science Journal (article, 2019-09-30)
Field: Chemistry — Organic Chemistry

The Comparison of Primary, Secondary and Tertiary Amine Ligands on Palladium (II) Complex Ion on Thermo-Physical, Chemical Reactivity, and Biological Properties: A DFT Study The Palladium is considered as the catalyst for coupling reaction and useful metal in industry. The thermo-physical, chemical reactivity and biological interaction are considered the most expected parameters for use in any area of the chemical industry, the pharmaceutical industry, and academia. The palladium (II) complex ion with different amine ligands are considered under theoretical study by the method of density functional theory (DFT). Some thermo-physical parameters such as free energy, entropy, dipole moment, binding energy, nuclear energy, electronics energy, heat of formation, reactivity properties of molecule like Highest Occupied Molecular Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO), HOMO-LUMO gap, ionization potential, electronegativity, hardness, softness and electron affinity, and biological properties of molecules like charge density, surface area grid, volume, LogP, polarizibility, refractivity, molecular mass, PIC50 were calculated using the computational program of DFT method. The value of HOMO LUMO gap is 10.78, 0.59, 0.50, and 10.73 and PIC50 is -20.41, -8.46, -1.69, and 1.83 for L01, L02, L03, and L04 respectively while the chemical stability is same for L02, and L03, similarly L01 and L04. The QSAR study provides information about their correlation and biological activity as drugs whereas the biological activity was increased with increasing methyl groups. The four palladium (II) complex ions with amine ligands have strong biological activity for L03 and L04, and occur the correlation on thermophysical, chemical reactivity. HOMO/LUMO ; Chemistry ; Electronegativity ; Palladium ; Density functional theory ; Electron affinity (data page) ; Computational chemistry ; Binding energy ; Molecule ; Physical chemistry ; Organic chemistry ; Catalysis ; Atomic physics Inorganic and Organometallic Chemistry ; Chemical Reaction Mechanisms ; Synthesis of Tetrazole Derivatives
Authors: M. Shaikh | Devendra S. Wagare | Mazahar Farooqui | Ayesha Durrani
Source: Asian Journal of Pharmacy and Pharmacology (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Rapid and environmentally benign protocol for the synthesis of pyrazolyl-4-thiazolidinone The multi-component one-pot approach becomes the need of the research due to its offer advantage like atom economy, minimum reaction time and most important one time workup. Therefore, there is an urgent need to design and developed the efficient and environmentally benign one-pot protocol for the synthesis of pyrazolyl-4thiazolidinone. Protocol (science) ; Chemistry ; Computer science ; Medicine Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis of heterocyclic compounds
Authors: Mohammed Golam Rasul
Source: Oriental Journal Of Chemistry (article, 2019-06-25)
Field: Chemistry — Organic Chemistry

Synthesis of Oxadiazole Derivative of Pentacyclic Triterpenoid and Its Biological Activity Oriental Journal of Chemistry is a peer reviewed quarterly research journal of pure and applied chemistry. It publishes standard research papers in almost all thrust areas of current chemistry of academic and commercial importance. It provides a platform for rapid publication of quality research papers, reviews and chemistry letters. Oriental Journal of Chemistry is abstracted and indexed in almost all reputed National and International agencies. Material chemistry ; Chemistry ; Elsevier Biobase ; Theoretical chemistry ; Industrial chemistry ; Editorial board ; Green chemistry ; Phytochemistry ; Library science ; Supramolecular chemistry ; Organic chemistry ; Medicinal chemistry ; Engineering ; Computer science ; Biochemical engineering Synthesis and biological activity
Authors: Daisy Selasteen F | Alfred Cecil Raj S
Source: Asian Journal of Pharmaceutical and Clinical Research (article, 2019-01-31)
Field: Chemistry — Organic Chemistry

IN VITRO STUDY OF ANTIMICROBIAL AND ANTIOXIDANT ACTIVITIES OF OXALIC ACID-DERIVED BIOACTIVE CHELATING AGENT Objective: The aim of this study is to evaluate the biological potentials of sodium cadmium oxalate dihydrate complex prepared by a single diffusion method in the silica gel medium.&#x0D; Methods: The present complex was derived by the oxalic acid using a single diffusion method in gel medium and tested for single-crystal X-ray diffraction, ultraviolet-visible (UV) spectrum (190–500 mm), and solubility (distilled water at 20–29°C) studies. Antioxidant activity was determined by 2, 2-diphenyl-1-picrylhydrazyl assay, and antimicrobial activity was measured by agar well diffusion method.&#x0D; Results: The present chelating complex was successfully synthesized by gel technique. The solubility of sodium cadmium oxalate dihydrate was moderately good for deionized warm water. The UV spectral studies confirmed the chelating O-H, Na-O, and C-O bonds of the newly synthesized complex for the optical and biological properties.&#x0D; Conclusions: The metal-oxygen and oxygen-hydrogen bonds of the present newly synthesized sample much improved its optical, antimicrobial, and antioxidant activities and find its applications in the field of pharmaceutical and biomedical applications. Chelation ; Chemistry ; Solubility ; Nuclear chemistry ; Antioxidant ; Distilled water ; Oxalic acid ; Oxalate ; Antimicrobial ; Cadmium oxide ; Inorganic chemistry ; Cadmium ; Organic chemistry ; Chromatography Inorganic and Organometallic Chemistry ; Field-Flow Fractionation Techniques ; Free Radicals and Antioxidants
Authors: Maruti S. Satpute | Vijay D. Gangan | I. Shastri
Source: International Journal of Scientific Research in Science and Technology (article, 2019-01-10)
Field: Chemistry — Organic Chemistry

Methyl Vanillate Ether Derivatives as Future Potential Drug Vanillic acid (4-hydroxy-3-methoxybenzoic acid) is a dihydroxybenzoic acid derivative used as a flavouring agent. It is used in the synthesis of various active pharmaceutical ingredients such as Etamivan, Modecainide, Brovanexine, Vanitiolide, Vanyldisulfamide etc. In this paper, novel ether derivatives of vanillic acid were synthesized and tested for potential antibacterial activity. This combinatorial synthesis of novel vanillic acid ether derivatives can be a useful approach to generate potent chemotherapeutic agents in developing new drug candidates. Vanillic acid ; Ether ; Chemistry ; Derivative (finance) ; Drug ; Combinatorial chemistry ; Organic chemistry ; Pharmacology ; Biology ; Business Synthesis and biological activity ; Biochemical and biochemical processes ; Computational Drug Discovery Methods
Authors: Saber Mohammadi Chalanchi | Ali Ebrahimi | Alireza Nowroozi
Source: Bulgarian Chemical Communications (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical insight to intermolecular hydrogen bond interactions between methyl N-(2-pyridyl) carbamate and acetic acid: substituent effects, cooperativity and energy decomposition analysis In the present work, the hydrogen bond (HB) interactions between substituted syn and anti rotamers of methyl N-(2-pyridyl) carbamate and acetic acid were investigated using quantum mechanical (QM) calculations. The rotamers have two typical active sites to form hydrogen bonds with acetic acid, such that four stable complexes are found on the potential energy surface. The complexes in which the oxygen atom of carbamate acts as proton acceptor are stabilized by EWSs and are destabilized by EDSs. The trend in the effects of substituents is reversed in the other two complexes, in which the nitrogen atom of ring is involved in the interaction. According to energy data, the substituent effects on the interaction energy can be expressed by Hammett constants. The natural resonance theory (NRT) model was used to investigate the charge distribution on the carbamate group and to discuss the interaction energies. The individual HB energies were estimated to evaluate their cooperative contributions on the interaction energies of the complexes. In addition, the localized molecular orbital energy decomposition analyses (LMO-EDA) demonstrate that the electrostatic interactions are the most important stabilizing components of interactions. Substituent ; Chemistry ; Hydrogen bond ; Conformational isomerism ; Intermolecular force ; Interaction energy ; Hydrogen atom ; Cooperativity ; Computational chemistry ; Photochemistry ; Stereochemistry ; Molecule ; Organic chemistry ; Alkyl Crystallography and molecular interactions ; Nonlinear Optical Materials Research ; Crystal structures of chemical compounds
Authors: Huanhuan Ju | Jiajing Sun | Xiaoliu Li | Hua Chen
Source: Chinese Journal of Organic Chemistry (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Synthesis of Aldo-quinazolinones by Iodine-Induced Oxidative Condensation of Aldoses and <i>o</i>-Aminobenzamides A series of aldo-quinazolinones were synthesized by a one-pot iodine-induced oxidative condensation of the unprotected mono-/di-saccharides and o-aminobenzamides in the yields of 30%80%, providing an effective protocol for the preparation of aldo-quinazolinones. The amount of iodine has an important effect on the reaction. Chemistry ; Iodine ; Condensation ; Oxidative phosphorylation ; Organic chemistry ; Medicinal chemistry ; Biochemistry Quinazolinone synthesis and applications ; Phenothiazines and Benzothiazines Synthesis and Activities ; Synthesis and Biological Evaluation
Authors: Bowen Li | Srinivas Rangarajan
Source: arXiv (Cornell University) (preprint, 2019-06-25)
Field: Materials Science — Materials Chemistry

Designing compact training sets for data-driven molecular property prediction In this paper, we consider the problem of designing a training set using the most informative molecules from a specified library to build data-driven molecular property models. Specifically, we use (i) sparse generalized group additivity and (ii) kernel ridge regression as two representative classes of models, we propose a method combining rigorous model-based design of experiments and cheminformatics-based diversity-maximizing subset selection within the epsilon--greedy framework to systematically minimize the amount of data needed to train these models. We demonstrate the effectiveness of the algorithm on subsets of various databases, including QM7, NIST, and a catalysis dataset. For sparse group additive models, a balance between exploration (diversity-maximizing selection) and exploitation (D-optimality selection) leads to learning with a fraction (sometimes as little as 15%) of the data to achieve similar accuracy as five-fold cross validation on the entire set. On the other hand, kernel ridge regression prefers diversity-maximizing selections. Computer science ; Machine learning ; Cheminformatics ; Set (abstract data type) ; Model selection ; Property (philosophy) ; Training set ; Kernel (algebra) ; Selection (genetic algorithm) ; Artificial intelligence ; Regression ; Data mining ; Heuristic ; Data set ; Greedy algorithm ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Alec H. Christian | Jia Shang | Patricia Zhang | Arismel Tena Meza | Matthew S. Sigman | Christopher J. Chang | F. Dean Toste
Source: ChemRxiv (preprint, 2019-05-24)
Field: Chemistry — Organic Chemistry

A Physical Organic Approach to Tuning Reagents for Selective and Stable Methionine Bioconjugation We report a data-driven, physical organic approach to the development of new methionine-selective bioconjugation reagents with tunable adduct stabilities. Statistical modeling of structural features described by intrinsic physical organic parameters was applied to the development of a predictive model and to gain insight into features driving stability of adducts formed from the chemoselective coupling of oxaziridine and methionine thioether partners through Redox Activated Chemical Tagging (ReACT). From these analyses, a correlation between sulfimide stabilities and sulfimide  (C=O) stretching frequencies was revealed. We ex-ploited the rational gains in adduct stability exposed by this analysis to achieve the design and synthesis of a bis-oxaziridine reagent for peptide stapling. Indeed, we observed that a macrocyclic peptide formed by ReACT stapling at methionine exhibited improved uptake into live cells compared to an unstapled congener, highlighting the potential utility of this unique chemical tool for thioether modification. This work provides a template for the broader use of data-driven approaches to bioconjugation chemistry and other chemical biology applications. Bioconjugation ; Thioether ; Reagent ; Chemistry ; Methionine ; Adduct ; Combinatorial chemistry ; Organic chemistry ; Amino acid ; Biochemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Synthesis and Catalytic Reactions
Authors: Baicheng Weng | Zhilong Song | Rilong Zhu | Qingyu Yan | Qingde Sun | Corey G. Grice | Yanfa Yan | Wan‐Jian Yin
Source: arXiv (Cornell University) (preprint, 2019-08-19)
Field: Materials Science — Materials Chemistry

Symbolic Regression Discovery of New Perovskite Catalysts with High Oxygen Evolution Reaction Activity Symbolic regression (SR) is an emerging method for building analytical formulas to find models that best fit data sets. Here, SR was used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. An unprecedentedly simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, was identified, which accelerated the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesized five new oxide perovskites and characterized their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, outperform the current state-of-the-art oxide perovskite catalyst, Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF). Our results demonstrate the potential of SR for accelerating data-driven design and discovery of new materials with improved properties. Perovskite (structure) ; Catalysis ; Oxygen evolution ; Oxide ; Octahedron ; Oxygen ; Materials science ; Regression ; Computer science ; Chemical engineering ; Nanotechnology ; Chemistry ; Physical chemistry ; Crystallography ; Metallurgy ; Mathematics ; Crystal structure ; Statistics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Zhaxylyk A. Kudyshev | Alexander V. Kildishev | Vladimir M. Shalaev | Alexandra Boltasseva
Source:  (article, 2019-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learning-Assisted Topology Optimization for Refractory Photonics We expand a conventional metasurface design methodology to a global optimization space by advancing topology optimization via artificial-intelligence-assisted algorithms and tailorable materials platforms. We apply the developed approach to design efficient refractory photonics for thermophotovoltaics. Topology optimization ; Photonics ; Computer science ; Topology (electrical circuits) ; Thermophotovoltaic ; Artificial intelligence ; Distributed computing ; Engineering ; Electronic engineering ; Materials science ; Finite element method ; Electrical engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Plasmonic and Surface Plasmon Research
Authors: Tetiana Bazhynova
Source: Bulletin of Kharkov National Automobile and Highway University (article, 2019-12-20)
Field: Materials Science — General Materials Science

Intelligent information and control system of hybrid and electric vehicles The methodology of building an intelligent information-control system by the power plant for hybrid and electric vehicles is considered in the paper. A distinctive feature of the methodology is the methods of artificial intelligence for solving the problems of “Movement” and the technology of intellectualization of the synthesis of “Recuperation” and “Movement” modes of hybrid and electric vehicles, implemented in the form of an integrated graph. Today, it remains an important solution to the problem of developing intelligent information and control systems for a hybrid and electric vehicle propulsion system. The purpose of functioning of the intelligent information management system of the power plant is to minimize energy consumption in the modes of “Recuperation” and “Movement”. The solution to these problems was carried out by means of the intellectualization technology for the synthesis of optimal control, implemented as an integrated graph. On the basis of the results, obtained at the stage of the analysis of the subject area, the construction of a promising structure of the intelligent control system of the hybrid and electric vehicle power plant a occurs, i.e. the basic subsystems are determined, the optimal composition of procedures in the functional modules of the subsystems, the mathematical, informational, technical and software are determined. At the same time, one of the most important criteria for identifying subsystems and functional modules is to minimize the number of information links of the system. The system is presented as a set of loosely coupled parts. Weak communication between the parts of the system means the absence of feedbacks, as well as the intensive exchange of information. Results. The methodological foundations of the synthesis of intellectualized optimal information management systems are formulated and justified. Intellectualization ; Control engineering ; Computer science ; Intelligent control ; Graph ; Control system ; Engineering ; Artificial intelligence ; Electrical engineering Material Properties and Applications ; Mathematical Control Systems and Analysis ; Advanced Data Processing Techniques
Authors: Mengdi Ju | Quan Qian
Source: Computational Materials Science (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Materials knowledge reasoning with production based system Corrosion ; Knowledge base ; Computer science ; Ontology ; Inference ; Inference engine ; Domain knowledge ; Materials science ; Knowledge-based systems ; Data mining ; Database ; Metallurgy ; Artificial intelligence Machine Learning in Materials Science ; Semantic Web and Ontologies ; Rough Sets and Fuzzy Logic
Authors: Yingguang Li | Jing Zhou | Di Li
Source:  (article, 2019-09-09)
Field: Chemistry — Organic Chemistry

A new concept to improve microwave heating uniformity through data-driven process modelling For a long time, the heating pattern of the workpiece within a multimode microwave oven was considered to be highly sophisticated. As a consequence, the uneven microwave heating problem can only be partly alleviated by a random movement between the electromagnetic field and the workpiece. In this paper, we reported that the heating pattern has a specific correspondence with microwave system settings. The influence factor of the heating pattern and the corresponding mechanism were systematically studied by both theoretical analysis and experimental investigations. On this basis, a data-driven process model was established to learn the material’s dynamic temperature behaviors under different microwave system settings, and a new concept to improve the microwave heating uniformity by temperature monitoring and active compensation was proposed. The effectiveness of the method was demonstrated by a polymer composite microwave processing case study. Microwave ; Process (computing) ; Microwave heating ; Compensation (psychology) ; Multi-mode optical fiber ; Materials science ; Electromagnetic heating ; Heating system ; Field (mathematics) ; Computer science ; Data processing ; Temperature measurement ; Process engineering ; Electronic engineering ; Mechanical engineering ; Engineering ; Electrical engineering ; Optical fiber ; Physics ; Telecommunications Microwave-Assisted Synthesis and Applications ; Epoxy Resin Curing Processes
Authors: Helena Stegherr | Anthony Stein | Joerg Haehner
Source: OPUS (Augsburg University) (article, 2019-05-20)
Field: Materials Science — Materials Chemistry

Parallel Chemical Reaction Optimisation for the Utilisation in Intelligent RNA Prediction Systems Traditional, strictly deterministic algorithms are reaching their limits as modern information systems are often challenged with tremendously complex optimisation tasks. This can be in terms of reasonable computational effort or due to the lack of gradient information. Therefore, a necessity of designing Intelligent Systems becomes apparent and the increasing appearance of such systems substantiates the currently observable advent of Artificial Intelligence. Thus, Computational Intelligence methods, such as metaheuristics, gain entry into modern systems in order to render the challenging problems tractable again, sometimes with the downside of only converging to approximately optimal solutions. An approach inspired by chemical reactions which attracted rather less research attention so far, especially with regard to the utilisation in Organic Computing research, has been proposed recently. In this working paper, the Chemical Reaction Optimisation (CRO) algorithm is transformed towards a parallel variant, called pACRO, in order to decrease convergence time and scalability by exploiting multicore computing architectures. The proposed parallel algorithm is empirically validated on well-known benchmark functions and set in relation to the findings reported in the literature. First results indicate that pACRO can compete with its predecessors. Furthermore, initial steps towards an application in the highly complex task of RNA Secondary Structure Prediction (RNA-SSP) are taken by outlining novel ways to construct the search space for helices as well as a solution candidate encoding based on which pACRO can be utilised. Computer science ; Scalability ; Benchmark (surveying) ; Set (abstract data type) ; Encoding (memory) ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Fuel Cells and Related Materials
Authors: Amanda S. Barnard | George Opletal
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Gold Nanoparticle Data Set This is a set of 4000 gold (Au) nanoparticle FINAL CONFIGURATIONS, for use in data-driven studies. These structures have been optimized (fully relaxed) using molecular dynamics with an embedded atom (EAM) interatomic potential, at various temperatures and growth rates. All files are in XYZ format, and the naming convention is defined in the accompanying csv file that lists all of the structural features and property indicators (see Supporting Attachments). Sizes range from 236 atoms to 14277 atoms, with both crystalline and non-crystalline configurations and regions. Each nanoparticle has been characterised using a variety of topological features, including size, lattice structure, surface curvature and a number of order parameters. The final two columns are target labels, providing the total energy and the excess formtion energy. Other features can also be used as labels as desired. Set (abstract data type) ; Data set ; Computer science ; Artificial intelligence Nanoparticles: synthesis and applications