jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan M. Skelton | David S. D. Gunn | Sebastian Metz | Stephen C. Parker
Source: Journal of Chemical Theory and Computation (preprint, 2020-05-05)
Field: Materials Science — Materials Chemistry

Accuracy of Hybrid Functionals with Non-Self-Consistent Kohn–Sham Orbitals for Predicting the Properties of Semiconductors Accurately modeling the electronic structure of materials is a persistent challenge to high-throughput screening. A promising means of balancing accuracy against computational cost is non-self-consistent calculations with hybrid density-functional theory, where the electronic band energies are evaluated using a hybrid functional from orbitals obtained with a less demanding (semi)local functional. We have quantified the performance of this technique for predicting the physical properties of 16 tetrahedral semiconductors with bandgaps from 0.2 to 5.5 eV. Provided the base functional predicts a nonmetallic electronic structure, bandgaps within 5% of the PBE0 and HSE06 gaps can be obtained with an order of magnitude reduction in computing time. The positions of the valence and conduction band extrema and the Fermi level are well reproduced, enabling calculation of the band dispersion, density of states, and dielectric properties using Fermi's Golden Rule. While the error in the non-self-consistent total energies is ∼50 meV atom<sup>-1</sup>, the energy-volume curves are reproduced accurately enough to obtain the equilibrium volume and bulk modulus with minimal error. We also test the dielectric-dependent scPBE0 functional and obtain bandgaps and dielectric constants to within 2.5% of the self-consistent results, which amounts to a significant improvement over self-consistent PBE0 for a similar computational cost. We identify cases where the non-self-consistent approach is expected to perform poorly and demonstrate that partial self-consistency provides a practical and efficient workaround. Finally, we perform proof-of-concept calculations on CoO and NiO to demonstrate the applicability of the technique to strongly correlated open-shell transition-metal oxides. Hybrid functional ; Density functional theory ; Atomic orbital ; Local-density approximation ; Electronic structure ; Dielectric ; Materials science ; Statistical physics ; Condensed matter physics ; Physics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Bao-Xin Xue | Mario Barbatti | Pavlo O. Dral
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Absorption Cross Sections We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties — excitation energies and oscillator strengths — are calculated with a reference electronic structure method only for relatively few points in the ensemble. Kernel-ridge-regression-based ML combined with the RE descriptor as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points. Computer science ; Algorithm ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Solid-state spectroscopy and crystallography
Authors: Xavier Désert
Source: theses.fr (ABES) (preprint, 2020-03-18)
Field: Chemistry — Organic Chemistry

Synthesis and kinetic studies of zirconocenes precatalysts used for the isoselective polymerization of α-olefins Les travaux de recherches décrits dans ce manuscrit s’intéressent à la différence de productivité entre deux familles de précatalyseurs utilisés pour la polymérisation homogène du propylène. Ces précatalyseurs sont des métallocènes du groupe IV : les complexes silylène bis(indényle)s ou complexes {SBI} et les complexes Cyclopentadiènyle/Fluorènyles ou {Cp/Flu}. Pour être actifs, ces précatalyseurs ont besoin d’un co-catalyseur : le MAO. À l’aide d’étude cinétique, les résultats de la première partie de ces recherches ont révélé que les complexes {SBI} ne disposent pas d’un plus grand nombre de sites actifs, pouvant expliquer leur meilleure productivité. Au contraire, les complexes {Cp/Flu} sont intrinsèquement plus actifs, mais semblent également plus sensibles à la formation de composés dormants avec le triméthylaluminium provenant du cocataylseur. La deuxième partie s’intéresse à la désactivation des systèmes {Cp/Flu} par la suite d’erreurs d’insertions du monomère lors de la propagation. Les investigations n’ont pas permis de montrer une tendance claire entre erreurs et productivité, mais permettent tout de même d’identifier l’influence de certains groupements sur l’activité catalytique. Polymerization ; Polymer chemistry ; Kinetic energy ; Chemistry ; Organic chemistry ; Polymer science ; Polymer ; Physics Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Inorganic and Organometallic Chemistry
Authors: Xavier Désert
Source: theses.fr (ABES) (article, 2020-03-27)
Field: Chemistry — Inorganic Chemistry

Synthèses et études cinétiques de précatalyseurs zirconocènes utilisés pour la polymérisation isosélective des α-oléfines The research work presented in this manuscript aims at understanding the different productivity behavior of two families of group IV precatalysts used in homogeneous polymerization of propylene. These two families include silylene bis(indenyle)s or {SBI} complexes and cyclopentadienyl/fluorenyl or {Cp/Flu} complexes. In order to be active, these metallocenes need a cocatalyst: the MAO. Thanks to kinetic studies, this work reveals that the better productivity of {SBI} complexes is not due to a better activation efficiency. In striking contrast, {Cp/Flu} precatalysts are intrinsically more active but also seem more prone to the formation of dormant adduct with trimethylaluminium issued from the cocatalyst. The second part of this work deals with the possibility of deactivation of {Cp/Flu} precatalysts as result of propagation errors. Although this work highlighted the influence of several structural modifications on the catalytic behavior of {Cp/Flu} complexes, no clear trend was observed between the number of propagation errors and productivity. Chemistry ; Philosophy ; Molecular biology ; Physics ; Biology Radioactive element chemistry and processing ; Asymmetric Hydrogenation and Catalysis ; Inorganic and Organometallic Chemistry
Authors: Liwei Cao | Mikhail Kabeshov | Steve Ley | Alexei A. Lapkin
Source: Apollo (University of Cambridge) (article, 2020-03-10)
Field: Chemistry — Organic Chemistry

In Silico Rationalisation of Selectivity and Reactivity in Pd-Catalysed C-H Activation Reactions A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and the electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling. Reactivity (psychology) ; Regioselectivity ; In silico ; Electrophile ; Selectivity ; Nucleophile ; Palladium ; Reaction mechanism Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Lina Bassiouny | Tamer Samir | Saied Abdallah | Hamdy Ashour | Ahmad Anwar
Source: Engineering Research Journal (article, 2020-07-01)
Field: Materials Science — General Materials Science

Effect of Nanoparticles on the Carbon fiber/Epoxy composite mechanical properties The mechanical properties of the carbon fiber/Epoxy composite were studied after the addition of nanoparticles for space applications. Three different nano-particles (Al₂O₃, MWCNT, and RGO) were dispersed in the epoxy matrix. Each sample was subjected to ICT electron beam radiation at a constant dose of 100 kGy. The mechanical properties of nanocomposites were tested by a universal tensile testing machine and were correlated to the variation of their constituents’ molecular structure obtained by the Fourier Transformation Infrared (FTIR) spectroscopy. The results revealed an enhancement in the mechanical properties of epoxy matrix after the addition of nanoparticles and preservation of the mechanical properties even after irradiation. Epoxy ; Composite number ; Composite material ; Carbon Nanoparticles ; Materials science ; Nanoparticle ; Fiber ; Carbon fibers ; Nanotechnology Material Properties and Applications ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Liwei Cao | Mikhail Kabeshov | Steve Ley | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2020-03-10)
Field: Chemistry — Inorganic Chemistry

In Silico Rationalisation of Selectivity and Reactivity in Pd-Catalysed C-H Activation Reactions A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and the electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling. Regioselectivity ; Chemistry ; Reactivity (psychology) ; Palladium ; Combinatorial chemistry ; Electrophile ; Catalysis ; Computational chemistry ; Selectivity ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Reactions and Isotopes ; Machine Learning in Materials Science
Authors: Elaheh Rahimpour | Sima Alvani-Alamdari | Abolghasem Jouyban
Source: Pharmaceutical Sciences (review, 2020-06-27)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review on Developed Pharmaceutical Analysis Methods by Iranian Analysts in 2018 This article summarizes the publishing activities including bioanalytical and pharmaceutical analyses researches carried out in Iran in 2018 in order to connect academic researchers to those in industry, medical care units and hospitals. A wide spectrum of analytical methods has been used to determine and/or evaluate drug levels in the biological samples, based on physical, chemical and biochemical principles. We have compiled a concise survey of the literature covering 125 reports and tabulated the relevant analytical parameters. Chromatographic and electrochemical methods were found to be the technique of choice for many workers and almost 83% studies were performed by using these methods. This is the first annual review of the literature searching in SCOPUS database for published bioanalytical and pharmaceutical analysis researches in Iran. Scopus ; Pharmaceutical care ; Pharmaceutical industry ; Bioanalysis ; Data science ; Management science ; Computer science ; Medicine ; MEDLINE ; Pharmacology ; Engineering ; Political science ; Family medicine ; Chemistry ; Pharmacy ; Chromatography Analytical Methods in Pharmaceuticals ; Electrochemical sensors and biosensors ; Pesticide Residue Analysis and Safety
Authors: FNU Srinidhi | Deepak Patel | Vasantha Kumara S A
Source: ChemRxiv (preprint, 2020-10-06)
Field: Chemistry — Spectroscopy

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed. Solubility ; Supercritical carbon dioxide ; Supercritical fluid ; Mean squared error ; Supercritical fluid extraction ; Approximation error ; Artificial neural network ; Carbon dioxide ; Chemistry ; Solvent ; Thermodynamics ; Mathematics ; Materials science ; Computer science ; Algorithm ; Statistics ; Organic chemistry ; Artificial intelligence ; Physics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Umit Volkan Ucak | Taek Kang | Junsu Ko | Juyong Lee
Source: ChemRxiv (preprint, 2020-07-31)
Field: Materials Science — Materials Chemistry

Substructure-based Neural Machine Translation for Retrosynthetic Prediction &lt;p&gt;This work presents a new template-free neural machine translation method for retrosynthetic reaction prediction by learning the chemical change at a substructural level. The proposed method effectively solves all the translation issues arising from SMILES-based representation of molecular structures.&lt;/p&gt; Retrosynthetic analysis ; Substructure ; Machine translation ; Translation (biology) ; Computer science ; Representation (politics) ; Artificial intelligence ; Natural language processing ; Transfer-based machine translation ; Machine learning ; Example-based machine translation ; Engineering ; Chemistry ; Stereochemistry ; Structural engineering Machine Learning in Materials Science ; Analytical Chemistry and Chromatography
Authors: Umit Volkan Ucak | Taek Kang | Junsu Ko | Juyong Lee
Source: ChemRxiv (preprint, 2020-07-31)
Field: Materials Science — Materials Chemistry

Substructure-based Neural Machine Translation for Retrosynthetic Prediction This work presents a new template-free neural machine translation method for retrosynthetic reaction prediction by learning the chemical change at a substructural level. The proposed method effectively solves all the translation issues arising from SMILES-based representation of molecular structures. Substructure ; Retrosynthetic analysis ; Machine translation ; Computer science ; Translation (biology) ; Artificial intelligence ; Representation (politics) ; Natural language processing ; Machine learning ; Engineering ; Chemistry ; Structural engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Chemical Synthesis and Analysis
Authors: Liwei Cao | Mikhail Kabeshov | Steve Ley | Alexei A. Lapkin
Source: Apollo (University of Cambridge) (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

In Silico Rationalisation of Selectivity and Reactivity in Pd-Catalysed C-H Activation Reactions A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and the electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling. Reactivity (psychology) ; Regioselectivity ; In silico ; Electrophile ; Selectivity ; Nucleophile ; Palladium ; Reaction mechanism Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Taylor M. Barrett
Source: ScholarlyCommons (University of Pennsylvania) (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Thioamides and Proteolysis: Examining the Effects and Applications of a Single-Atom Substitution Thioamide substitution in the peptide backbone enables several applications in studying proteolysis, monitoring protease activity and in selective protease inhibition. To date, we have used model substrates to investigate the positional effects that these thioamide substitutions have on proteolysis rates, as well to examnine the ability of thioamides to stabilize peptides for imaging applications. In order to determine the positional effects of thioamides, we have examined several model peptides with a thioamide scanned from the P3 position to the P3' position and identified thioamide perturbing and non-perturbing positions. Further mechanistic investigation showed that the primary effect of the thioamide is often to prevent binding to the protease. Finally, incorporation of a thioamide into a peptide scaffold that is known to selectively bind to the neuropeptide Y Y1 receptor increased its serum stability, without affecting its affinity for the receptor. Currently, we are optimizing this stabilized imaging peptide usage in animals by changing linkers, fluorophores, and N-terminal modifications. We are also developing a system where full-length peptide hormones can be expressed, stabilized via thioamidation, and tagged with a fluorophore to afford in vivo peptide hormone imaging agents. Together, these studies provide guidelines for positioning thioamides for various proteolysis applications and show that thioamides can be used to create stabilized imaging peptides. Substitution (logic) ; Proteolysis ; Chemistry ; Computer science ; Biochemistry Synthesis and Reactivity of Sulfur-Containing Compounds ; Synthesis and Characterization of Heterocyclic Compounds ; Chemical Synthesis and Analysis
Authors: Federico Serrano Sánchez | Ting Luo | Junjie Yu | Wenjie Xie | Gudrun Auffermann | Anke Weidenkaff | Tiejun Zhu | Xinbing Zhao | J. A. Alonso | Baptiste Gault | Claudia Felser | Chenguang Fu
Source: ChemRxiv (preprint, 2020-04-29)
Field: Materials Science — Materials Chemistry

Improved Thermoelectric Performance in N-Type Half-Heusler NbCoSn by Heavy-Element Pt Doping Half-Heusler compounds with a valence electron count of 18, including ZrNiSn, ZrCoSb, and NbFeSb, are good thermoelec-tric materials owing to favourable electronic structures. Previous computational studies had predicted a high electrical power factor in another half-Heusler compound NbCoSn, but it has not been extensively investigated experimentally. Herein, the synthesis, structural characterization, and thermoelectric properties of the heavy-element Pt-doped NbCoSn compounds are reported. Pt is found to be an effective dopant enabling the optimization of electrical power factor, simul-taneously leading to a strong point defect scattering of phonons, and thereby suppressing the lattice thermal conductivity. Annealing significantly improves the carrier mobility, which is ascribed to the decreased grain boundary scattering. As a result, a maximum power factor of ~3.4 mWm-1K-2 is obtained at 600 K. In conjunction with the reduced lattice thermal conductivity, a maximum figure of merit zT of ~0.6 is achieved at 773 K for the post-annealed NbCo0.95Pt0.05Sn, an increase of 100% compared to the undoped NbCoSn. This work highlights the important roles of the doping element and micro-structure on the thermoelectric properties of half-Heusler compounds&lt;br&gt;&lt;p&gt;&lt;/p&gt; Thermoelectric effect ; Materials science ; Phonon scattering ; Doping ; Dopant ; Thermoelectric materials ; Condensed matter physics ; Annealing (glass) ; Electron mobility ; Electrical resistivity and conductivity ; Grain boundary ; Figure of merit ; Seebeck coefficient ; Thermal conductivity ; Heusler compound ; Phonon ; Scattering ; Optoelectronics ; Metallurgy ; Metal ; Electrical engineering ; Composite material ; Optics ; Physics ; Thermodynamics ; Microstructure Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials