jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Alber | Loai Nasrat | Hanafy Ismail | Medhat M. Hassan | A Rashid | J Saleem | M Amin | S Ali | N Kamarudin | J Razak | N Mohamad | N Norddin | A Aman | M Ismail | I Plea | P Noingher | S Schlgl | C Sumereder | M Muhr | M Ali | R Hackam | J Vas | B Venkatesulu | M Thomas | L Lan | G Yao | H Wang | X Wen | Z Liu | G Haddad | R Gupta | K Wong | Y Kurata | K Takano | K Sakuraba | M Hayashi | S Simmons | M Shah | J Mackevich | R Chang | M Brown | L Nasrat | R Sharkawy | R Deepalaxmi | M Balaji | V Rajini | M Ehsani | H Borsi | E Gockenbach | G Bakhsahnde | J Morshedian | N Abedi | R Prabu | S Usa | K Udayakumar | M Khan | S Majeed | Z Li | K Okamoto | Y Ohki | T Tanaka | C Pan | K Wu | G Chen | Y Gao | M Florkowski | Z Lv | T Lewis | M Frechette | D Agoris | A Campus | J Castellon | J Densley | S Raetzke | J Kindersberger | M Balachandran | S Devanathan | R Muraleekrishnan | S Bhagawan | J Seiler | T Yu | P Zhang | Q Shao | S Azizi | G Momen | C Ouellet-Plamondon | David | L Saldivar-Guerrero R, Rejon | M Nazir
Source: International Journal of Advanced Technology and Engineering Exploration (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Electromechanical characterisation of nano silica filled EPDM-SiR composite for high voltage insulation The most important requirement for a power distribution system is effective insulation since a reliable power supply depends heavily on the quality of the insulation In recent decades, polymer insulators have been increasingly popular and acceptable for use in high voltage (HV) insulation. The significant benefits that a polymeric insulator provides are lightweight, enhanced efficiency in a polluted environment, ease of handling, and hydrophobicity [2, 3]. Materials science ; Composite material ; Nano- ; Composite number ; Nanocomposite High voltage insulation and dielectric phenomena ; Concrete Corrosion and Durability ; Tribology and Wear Analysis
Authors: Andrea Tirelli | Giacomo Tenti | Kousuke Nakano | Sandro Sorella
Source: Physical review. B./Physical review. B (article, 2022-07-08)
Field: Materials Science — Materials Chemistry

High-pressure hydrogen by machine learning and quantum Monte Carlo We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a machine learning potential (MLP). We use kernel regression in combination with the smooth overlap of atomic position (SOAP) features, implemented here in a very efficient way. The key ingredients are as follows: (i) a sparsification technique, based on farthest point sampling, ensuring generality and transferability of our MLPs, and (ii) the so-called $\mathrm{\ensuremath{\Delta}}$--learning, allowing a small training data set, a fundamental property for highly accurate but computationally demanding calculations, such as the ones based on quantum Monte Carlo. As an application we present a benchmark study of the liquid-liquid transition of high-pressure hydrogen and show the quality of our MLP, by emphasizing the importance of high accuracy for this very debated subject, where experiments are difficult in the laboratory, and theory is still far from being conclusive. Computer science ; Benchmark (surveying) ; Monte Carlo method ; Quantum Monte Carlo ; Artificial intelligence ; Kernel (algebra) ; Machine learning ; Quantum ; Algorithm ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Phase Equilibria and Thermodynamics
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: ChemRxiv (preprint, 2022-08-31)
Field: Materials Science — Materials Chemistry

Pauling Number as Efficient Descriptors for Screening the Structures with Negative Thermal Expansion Characteristics In this study, the analysis of the known structure types demonstrating NTE was performed. Describing these structure types by the recently introduced and manifested parameter, Pauling Number (PN), showed that it is possible to circumscribe the candidate compounds with these characteristics by using this structure parameter since the flexibility of the compounds framework and the lattice topology are highly associated with the possibility of materials do not expand the lattice parameters while heating and to limit the changes of the structure during the possible phase transitions by, e.g., polyhedral tilting. We suggest that compounds with PN value close to those of the NTE materials can be considered as the targets for further investigation. Lattice (music) ; Negative thermal expansion ; Flexibility (engineering) ; Limit (mathematics) ; Statistical physics ; Thermal ; Topology (electrical circuits) ; Thermal expansion ; Mathematics ; Physics ; Materials science ; Combinatorics ; Thermodynamics ; Mathematical analysis ; Statistics Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Alessandra del Masto
Source:  (preprint, 2022-10-31)
Field: Materials Science — Materials Chemistry

Bulk and Point Defect Properties in α-Zr: Uncertainty Quantification on a Semi-Empirical Potential † Modelling studies of irradiation defects in &amp;alpha;-Zr, such as point defects and their multiple clusters, often use semi-empirical potentials because of their higher computational efficiency as compared to ab initio approaches. Such potentials rely on a fixed number of parameters that need to be fitted to a reference dataset (ab initio and/or experimental), and their reliability is closely related to the uncertainty associated with their parameters, coming from both data inconsistency and model approximations. In this work, parametric uncertainties are quantified on a Second Moment Approximation (SMA) potential, focusing on bulk and point defect properties in &amp;alpha;-Zr. A surrogate model, based on polynomial chaos expansion, is first built for properties of interest computed from atomistics, and simultaneously allows us to analytically compute the sensitivity indices of the observed properties to the potential parameters. This additional information is then used to select a limited number of material properties for the Bayesian inference. The posterior probability distributions of the parameters are estimated through two Markov Chain Monte Carlo (MCMC) sampling algorithms. The estimated posteriors of the model parameters are finally used to estimate materials properties (not used for the inference): in any case, most of the properties are closer to the reference ab initio and experimental data than those obtained from the original potential. Parametric statistics ; Markov chain Monte Carlo ; Statistical physics ; Inference ; Monte Carlo method ; Uncertainty quantification ; Bayesian inference ; Algorithm ; Ab initio ; Point estimation ; Bayesian probability ; Moment (physics) ; Computer science ; Applied mathematics ; Mathematics ; Statistics ; Physics ; Artificial intelligence Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Shiyi Tang | Jiahao Jue | Yifan Lin
Source: Highlights in Science Engineering and Technology (article, 2022-12-27)
Field: Chemistry — Analytical Chemistry

Study on Identification Method of Chinese Herbal Medicine based on Infrared Spectroscopy Characteristics The identification of Chinese herbal medicines is a key issue in the field of traditional Chinese medicine. Based on the characteristics of Chinese herbal medicines, the classification of types, producing areas, and quality can be realized. However, traditional identification methods of Chinese herbal medicines mainly rely on manual identification methods, which requires a lot for identification personnel with low efficiency. To solve this problem, we study the intelligent method of identification of Chinese herbal medicines by using data of infrared spectroscopy characteristic. To solve this problem, this paper studies the classification of spectral characteristic data of Chinese herbal medicines from unsupervised and supervised learning. Firstly, an improved K-means clustering algorithm based on Gaussian distribution model is established for unsupervised spectral classification of Chinese herbal medicines. This method “over-classifies” the sample data by K-means clustering algorithm, and further classifies the data by Gaussian mixture model, thus realizing unsupervised classification of Chinese herbal medicines. Secondly, aiming at the supervised classification and recognition of Chinese herbal medicines, an improved discriminant analysis classification method based on Gaussian distribution is established to identify different kinds and producing areas of Chinese herbal medicines. Finally, we test our method on two sets of data with and without tagged information, with Chinese herbal medicines in two data sets identified respectively. The experimental results fully verify the effectiveness of the method, especially in the supervised identification of Chinese herbal medicines. We have proved the effectiveness of our designed model through the comparison of various methods and extensive tests. Identification (biology) ; Artificial intelligence ; Cluster analysis ; Traditional medicine ; Traditional Chinese medicine ; Linear discriminant analysis ; Computer science ; Pattern recognition (psychology) ; Gaussian ; Machine learning ; Data mining ; Medicine ; Alternative medicine Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Traditional Chinese Medicine Studies
Authors: Jiahao Zhang | Haiyue Peng | Zi'an Chen | Guorui Li | Jing Huang
Source: ChemRxiv (preprint, 2022-05-13)
Field: Chemistry — Organic Chemistry

Photocaged Activity-Based Probes for Spatiotemporal Detection of Protein S-sulfenylation in Living Cells Protein cysteine residues have unique reactivity due to the low redox potential of its thiol side chain. Protein S-sulfenylation (protein sulfenic acid), as one of the most significant oxidative post-translational modifications (OxiPTMs), plays a vital role in regulating protein function. Due to the transient presence of sulfenic acid in living cell, many detecting methods have been limited. Activity-based probes provide powerful tools to elucidate this process, so their discovery has been at the forefront of redox biology. In this study, two caged cysteine sulfenic acid probes DYn-2-ONB, DYn-2-Cou with either an o-nitrobenzyl or coumarin protecting group were developed. Both probes can be efficiently uncaged via irradiation to produce the active C-nucleophile probe DYn-2. Labeling assay in living cells demonstrated DYn-2-ONB exhibited better labeling capacity compared with DYn-2, providing it as a powerful tool to detect protein S-sulfenylation in spatio-temporally controllable manner. Sulfenic acid ; Cysteine ; Chemistry ; Thiol ; Biochemistry ; Nucleophile ; Coumarin ; Combinatorial chemistry ; Enzyme ; Organic chemistry Click Chemistry and Applications ; Sulfur-Based Synthesis Techniques ; Sulfur Compounds in Biology
Authors: Hannu T. Vuori | J. Mikko Rautiainen | Erkki Kolehmainen | Heikki M. Tuononen
Source: Dalton Transactions (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Computational thermochemistry: extension of Benson group additivity approach to organoboron compounds and reliable predictions of their thermochemical properties High-level computational data for standard gas phase enthalpies of formation, entropies, and heat capacities are reported for 116 compounds of boron. A comparison of the results with extant experimental and computational benchmark values reveals important trends and clear outliers. Recommendations are made to revise some of the key quantities, such as the enthalpies of formation of orthoboric acid, trimethylthioborate, and triphenylborane, the last of which is found to be considerably in error. The uncertainties associated with the experimental values are found to exceed those of high-level calculations by a clear margin, prompting the redetermination of Benson group additivity contributions for boron-based groups on purely computational grounds. The applicability of the established group contribution values is demonstrated by estimating thermochemical data for large organoboron compounds that cannot be treated with high-level quantum chemical methods and comparing the results with existing experimental and computational values. Thermochemistry ; Additive function ; Extension (predicate logic) ; Computational chemistry ; Group (periodic table) ; Chemistry ; Standard enthalpy of formation ; Organoboron compounds ; Thermodynamics ; Organic chemistry ; Mathematics ; Physical chemistry ; Computer science ; Physics ; Catalysis Crystallography and molecular interactions ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Евгения Викторовна Антипина | Светлана Мустафина | Андрей Федорович Антипин
Source: Вестник Тверского государственного университета Серия Химия (article, 2022-07-07)
Field: Chemistry — Organic Chemistry

THEORETICAL OPTIMIZATION OF REGULAR PARAMETERS OF A CATALYTIC REACTION WITH A VARIABLE REACTION VOLUME Статья посвящена разработке методов поиска оптимального температурного режима каталитической реакции с переменным реакционным объемом. В работе в общем виде сформулирована задача теоретической оптимизации каталитической реакции, в которой управляющим параметром является температура реакции, фазовыми переменными - значения концентраций веществ и относительное изменение числа молей реакционной среды. Для поиска численного решения поставленной задачи оптимального управления с ограничением на управляющий параметр предложен алгоритм, основанный на методе дифференциальной эволюции. Работа алгоритма апробирована на каталитической реакции синтеза бензилиденбензиламина. Описана математическая модель реакции с учетом изменения реакционного объема в ходе протекания реакции, на основе которой сформулирована задача поиска оптимального температурного режима с целью получения максимальной концентрации продукта реакции - бензилиденбензиламина. Получены оптимальный температурный профиль и оптимальные концентрации реагентов. The article is devoted to the development of methods for finding the optimal temperature regime for a catalytic reaction with a variable reaction volume. In this work, the problem of theoretical optimization of a catalytic reaction is formulated in general terms, in which the control parameter is the reaction temperature, the phase variables are the concentrations of substances and the relative change in the number of moles of the reaction medium. To search for a numerical solution of the formulated problem of optimal control with a constraint on the control parameter, an algorithm based on the differential evolution method is proposed. The operation of the algorithm was tested on the catalytic reaction of the synthesis of benzylidenebenzylamine. A mathematical model of the reaction is described taking into account the change in the reaction volume during the course of the reaction, on the basis of which the problem of finding the optimal temperature regime in order to obtain the maximum concentration of the reaction product, benzylidenebenzylamine, is formulated. The optimal temperature profile and optimal concentrations of reagents were obtained. Catalysis ; Reagent ; Work (physics) ; Optimal control ; Reaction rate ; Volume (thermodynamics) ; Thermodynamics ; Constraint (computer-aided design) ; Variable (mathematics) ; Chemical kinetics ; Chemistry ; Reaction conditions ; Mathematics ; Applied mathematics ; Mathematical optimization ; Kinetics ; Physical chemistry ; Mathematical analysis ; Organic chemistry ; Physics ; Classical mechanics Inorganic and Organometallic Chemistry ; Synthesis and Characterization of Heterocyclic Compounds ; Chemistry and Chemical Engineering
Authors: N. Gothard | Michael P. Steurer | Hannah Lovegrove | Autumn Moffitt | Asher Chok | Luke J. Bissell
Source: physica status solidi (b) (article, 2022-12-03)
Field: Materials Science — Materials Chemistry

Density Functional Theory Search of 3<i>d</i> Transition Metal Complexes in Diamond for Quantum Sensing Applications Optically addressable defects in wide‐bandgap semiconductors are of considerable interest as materials platforms for quantum sensing and information systems. Density functional theory is used to identify novel defects in diamond consisting of transition metal + nitrogen complexes with potential for quantum sensing. Defects are characterized with respect to formation energy, ordering and degeneracy of the defect energy levels, optical and charge transition energies, zero‐field‐splitting energies, and thermodynamic stability. Novel defect sites with optically addressable spin states and viable spin sublevel configurations are identified, and analysis of activation energies for atomic migration indicates stability in the lattice. Density functional theory ; Diamond ; Transition metal ; Semiconductor ; Materials science ; Condensed matter physics ; Band gap ; Degeneracy (biology) ; Lattice (music) ; Quantum ; Chemical physics ; Energy level splitting ; Physics ; Chemistry ; Optoelectronics ; Computational chemistry ; Quantum mechanics Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Pavel Potocek | Maurice Peemen | Bert Freitag | Philipp Müller | Thorsten Wieczorek
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Fast Automatic Analytical Particle Analysis Using an AI Guided Smart Scan Strategy The shape, size, chemical composition and distribution of nanoparticles influence their properties and function -especially in catalysis applications. The relevance of such measurements depends greatly on a statistically significant analysis on sufficiently large numbers of particles. Therefore, an automatic, fast, and reliable way of characterizing these parameters is pivotal in nanoparticle research. We demonstrated a relative speed up of two orders of magnitude compared to the classical procedure based on full frame XEDS mapping. Particle (ecology) ; Computer science ; Content (measure theory) ; Action (physics) ; Physics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Characterization and Applications of Magnetic Nanoparticles ; Electrostatics and Colloid Interactions
Authors: J. Bulin | Jan Hamaekers | M. P. Ariza | M. Ortiz
Source: arXiv (Cornell University) (preprint, 2022-08-06)
Field: Materials Science — Materials Chemistry

Interatomic-Potential-Free, Data-Driven Molecular Dynamics We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e.g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i.e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm. Verlet integration ; Molecular dynamics ; Interatomic potential ; Force field (fiction) ; Statistical physics ; Convergence (economics) ; Computer science ; Range (aeronautics) ; Field (mathematics) ; Physics ; Quantum mechanics ; Mathematics ; Materials science Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: N. Vasudevan | Karthick Thiyagarajan
Source: Revue d intelligence artificielle (article, 2022-12-23)
Field: Chemistry — Analytical Chemistry

Grape Leaves Segmentation Using an Improved Graph-Based Approach The study of plant pictures is very beneficial to agriculture and is sustainable. It is used to precisely and often record data on plant development, yield, breadth, and height of the plant, leaf area, etc. The quantity of leaves in the plants directly affects plant development, which is one of the most important characteristics to be examined among these plant characteristics. To extract leaves from photos of grape plants, a novel technique known as an enhanced graph-based approach is proposed in this study. The suggested procedure comprises two phases. Red, Green, and Blue (RGB) to Hue, Saturation, and Value (HSV) conversion and background removal are required in the initial phase for picture improvement. In the second stage, a novel graph-based technique and the Circular Hough Transform (CHT) are used to extract the leaf area from the plant picture. Theni District, Tamilnadu, India's grape leaf real-time datasets have been used in the experimentation for the suggested study. The segmentation pixel accuracy of the suggested approach is 92.4%, and the Mean Intersection over Union (MIoU) value is 86.2%, which is superior to the methods currently in use. Segmentation ; Graph ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Theoretical computer science Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research
Authors: Candice N. Carneiro | Federico J.V. Gómez | Adrián Spisso | María Fernanda Silva | Jorge L.O. Santos | Fábio de S. Dias
Source: Research Square (preprint, 2022-10-17)
Field: Chemistry — Analytical Chemistry

Exploratory analysis of South America wines using artificial intelligence Abstract The microwave-induced plasma optical emission spectrometer has been used for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentine wine and the application of logistic regression, support vector machine, and decision tree for exploratory analysis and the comparison of these algorithms in differentiating red wine samples by region of origin. The Brazilian wines samples were clustering in a group, while Argentines wines samples formed another cluster. The quantification limits (mg L -1 ) were P (0.06); B (0.08); K (0.17); Mn (0.002); Cr (0.002) and Al (0.02). The accuracy of the method was evaluated analyzing the wine samples by ICP OES for results comparison. The concentration in mg L -1 found for each element in wine samples, were Al (&lt; 0.02 - 1.82), Cr (0.15 - 0.50), Mn (&lt; 0.002 - 0.8), P (97 - 277), B (1.7 - 11.6), Pb (&lt; 0.06 - 0.3), Na (8.84 - 41.57) and K (604 - 1701), in mg L -1 . Wine ; Exploratory analysis ; Chemistry ; Analytical Chemistry (journal) ; Mathematics ; Chromatography ; Mineralogy ; Food science ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies