jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jason Meziere | Yu Luo | Yi Zia | LK Beland | MR Daymond | Gus L. W. Hart
Source: arXiv (Cornell University) (preprint, 2023-04-03)
Field: Materials Science — Materials Chemistry

Accelerating Training of MLIPs Through Small-Cell Training While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the $α$-$β$ pure zirconium phase transition in molecular dynamics simulations. Compared to traditional active learning, small-cell training decreased the training time of the $α$-$β$ zirconium phase transition by approximately 20 times. The potential describes the phase transition with a degree of accuracy similar to that of the large-cell training method. Training (meteorology) ; Computer science ; Construct (python library) ; Molecular dynamics ; Phase (matter) ; Protocol (science) ; Phase transition ; On the fly ; Density functional theory ; Artificial intelligence ; Alpha (finance) ; Resource (disambiguation) ; Machine learning ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Thermodynamics ; Construct validity ; Statistics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Fuel Cells and Related Materials
Authors: Imran Ali | Faisal Farooq | Md Khairul Wara | Rudhima Raj
Source: International Journal of Chemical and Environmental Sciences (article, 2023-10-03)
Field: Chemistry — Organic Chemistry

Synthesis, Characterisation and Microbial Activity of 2-(1H-benzothiazol-2-yl) Thioacetic acid with some Bivalent Metal Ions The complexes of the potassium salt of 2-mercaptobenzthiazole with potassium salt of 2-chloroacetic acid in aqueous dioxane at reflux temperatures result in the formation of potassium salt of 2-(1H- benzolthiazol-2-yl) thioacetic acid (K C 9 H 6 NO 2 S 2 =KL). The Ligand KL forms stable complexes with bivalent metal chloride in aqueous ethanol to form bisligated complexes [ML 2 ]nH 2 O [(M=Co(II), Ni(II), Mn(II), Cu(I), Zn(II), or Cd(II) and n=o, 2 or 4)]. The Magnetic susceptibility and electronic spectral studies of Co(II), Mn(II), Ni(II), and Cu(II) complexes resemble their octahedral or distorted octahedral structure of these metal ions. The i. r spectral bands positions of complexes indicated that benzothiazole part of the ligand is not involved in coordination. The antifungal activity of ligand and its complexes were examined by cup and disc method. The results do not show appreciable antifungal activity examined by cup and disc method. The results do not show appreciable antifungal activity examined on fungi. The thermal stability of complexes indicated that two water molecules in Co(II), Ni(II) Cu (II) and Mn(II) Complexes [ML 2 (H 2 O) 2 ]2H 2 O are present in coordination sphere. Chemistry ; Octahedron ; Metal ; Aqueous solution ; Metal ions in aqueous solution ; Ligand (biochemistry) ; Potassium ; Coordination complex ; Medicinal chemistry ; Coordination sphere ; Inorganic chemistry ; Chloroacetic acid ; Molecule ; Nuclear chemistry ; Stereochemistry ; Crystallography ; Crystal structure ; Organic chemistry ; Receptor Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: S RUCKMANI | M SANTHI | B. Divya
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

DEEP RESIDUAL LEARNING FOR RECOGNISING ADULTERATION IN PULSES VIA THERMAL IMAGING Food is the most essential requirement for sustenance of human life and it is one of the basic necessities of life. For optimal health, we should consume pure, nourishing, and adulterant-free food. Adulteration is the malicious contamination of food products with inferior, less expensive, inedible, or hazardous ingredients. Food adulteration means adding harmful substances to food products to contaminate or adulterate it. Pulses are a significant part of Indian cuisine, serving as a staple food item. Pulses may be adulterated by accidental, commercial, or metallic components, which can be harmful to health, rendering them unsafe for consumption and posing health risks. In order to guarantee the safety and quality of food, it is necessary to analyse pulses for any potential adulteration. In this paper, we propose a method for determining adulteration in Dal (Lentils) using ResNet-50, a deep learning model based on CNN. For this work, thermal images of pure dal and adulterated dal were used to classify the adulterated and unadulterated samples. Experimental results show that the proposed system can achieve high accuracy in detecting adulteration in pulses. The proposed system is also fast and requires minimal human intervention, making it suitable for use in food quality control systems. Key Words: Adulteration, Deep learning, Thermal Images, Convolutional Neural Network (CNN), Residual Network, Pulses, lentils. Residual ; Adulterant ; Convolutional neural network ; Food safety ; Deep learning ; Food products ; Computer science ; Artificial intelligence ; Agricultural engineering ; Food science ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Saravanan | K. Kumararaja
Source: Journal of Manufacturing Engineering (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Performance of ANN in Predicting the Tensile and Shear Strength of Al-Steel Explosive Clads In this study, an artificial neural network (ANN) model is created to predict aluminium-stainless steel explosive clads' tensile and shear strengths. The parameters for the explosive cladding process, such as the loading ratio (mass ratio of the explosive and the flyer, 0.6-1.0), standoff distance (5-9 mm), preset angle (0°-10°), and groove in the base plate (V/Dovetail), were altered. The ANN algorithm was trained in Python using the tensile and shear strengths gathered from 80% of the experiments (60), trials, and prior results. The constructed model was evaluated utilizing the remaining experimental results. The ANN model successfully predicts the tensile and shear strengths with an accuracy of less than 10% deviation from the experimental result. Explosive material ; Ultimate tensile strength ; Materials science ; Shear (geology) ; Composite material ; Structural engineering ; Aluminium ; Engineering ; Chemistry High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Peter L. Freddolino | Eric F. Bell | Jacob T. Schwartz | Manasa Yadavalli | Catherine Barnier
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

High throughput structure-guided functional annotation of microbial proteomes Annotation ; Computational biology ; Proteome ; Protein sequencing ; Sequence (biology) ; DNA sequencing ; Function (biology) ; Structural genomics ; Similarity (geometry) ; Biology ; Protein function ; Computer science ; Massive parallel sequencing ; Bioinformatics ; Peptide sequence ; Genetics ; Protein structure ; Artificial intelligence ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Rico Friedrich | Stefano Curtarolo
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

AFLOW-CCE for the thermodynamics of ionic materials Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parameterization of DFT errors with respect to coordination numbers and cation oxidation states present a simple, yet accurate solution to enable materials stability assessments. Here, we illustrate the computational capabilities of our AFLOW-CCE software by utilizing our previous results for oxides and introducing new results for nitrides. The implementation reduces the deviations between theory and experiment to the order of the room temperature thermal energy scale, i.e. ~25 meV/atom. The automated corrections for both materials classes are freely available within the AFLOW ecosystem via the AFLOW-CCE module, requiring only structural inputs. Ionic bonding ; Density functional theory ; Stability (learning theory) ; Atom (system on chip) ; Nitride ; Thermodynamics ; Thermal stability ; Standard enthalpy of formation ; Statistical physics ; Computer science ; Materials science ; Applied mathematics ; Chemistry ; Computational chemistry ; Physics ; Ion ; Mathematics ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science ; Ionic liquids properties and applications ; Thermal and Kinetic Analysis
Authors: Allyn M. Xu | Lauren C. Tang | Marko Jovanović | Oded Regev
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-03)
Field: Chemistry — Spectroscopy

A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based high-throughput approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (number of basic sites). We find that these peptides can be classified into two regimes-undercharging and overcharging-and that these two regimes display markedly different charging characteristics. Strikingly, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification. Electrospray ionization ; Analyte ; Mass spectrometry ; Ionization ; Peptide ; Chemistry ; Electrospray ; Chemical physics ; Biological system ; Chromatography ; Analytical Chemistry (journal) ; Biology ; Ion ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Wu-Rong Jian | Mian Xiao | WaiChing Sun | Wei Cai
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

Prediction of Yield Surface of Single Crystal Copper from Discrete Dislocation Dynamics and Geometric Learning A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs through the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors using DDD simulations and a geometric manifold learning approach. The yield surfaces in the three-dimensional space of plane stress are constructed for single-crystal copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions, respectively. With increasing plastic deformation under $[100]$ loading, the yield surface expands nearly uniformly in all directions, corresponding to isotropic hardening. In contrast, under $[110]$ loading, latent hardening is observed, where the yield surface remains nearly unchanged in the orientations in the vicinity of the loading direction itself, but expands in other directions, resulting in an asymmetric shape. This difference in hardening behaviors is attributed to the different dislocation multiplication behaviors on various slip systems under the two loading conditions. Materials science ; Yield surface ; Isotropy ; Hardening (computing) ; Dislocation ; Yield (engineering) ; Copper ; Single crystal ; Crystallography ; Slip (aerodynamics) ; Glide plane ; Plasticity ; Strain hardening exponent ; Dislocation creep ; Composite material ; Deformation (meteorology) ; Stress space ; Mechanics ; Geometry ; Metallurgy ; Thermodynamics ; Optics ; Mathematics ; Finite element method ; Physics ; Chemistry Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Ulrich Woiwode | Adrian Sievers‐Engler | Michael Lämmerhofer
Source: Electrophoresis (article, 2023-11-02)
Field: Chemistry — Spectroscopy

Cross‐linked polysiloxane‐coated stable bond <i>O</i>‐9‐(2,6‐diisopropylphenylcarbamoyl)quinine and quinidine chiral stationary phases as well as application in enantioselective cryo‐HPLC In this work, brush-type chiral stationary phases (CSPs) with O-9-(2,6-diisopropylphenylcarbamoyl)-modified quinidine (DIPPCQD-brush/-SH) and O-9-(2,6-diisopropylphenylcarbamoyl)-modified quinine (DIPPCQN-brush/-SH) were prepared as benchmarks for comparison with new corresponding polymeric CSPs with more stable bonding chemistry. These polymeric CSPs were prepared by coating a thin poly(3-mercaptopropyl)-methylsiloxane film together with the chiral selector onto vinyl-modified silica. In a second step, immobilization of the quinine/quinidine derivatives as well as cross-linking of the polysiloxane film to the vinyl-silica is achieved by a double thiol-ene click reaction. The polymeric CSPs exhibited similar enantioselectivity as the corresponding brush phases, but showed lower chromatographic efficiencies. Chiral acidic substances were separated into enantiomers (e.g., N-protected amino acids, herbicides like dichlorprop) in accordance with an enantioselective anion-exchange process. Oxidation of residual thiol groups of the polymer DIPPCQN-CSP introduced sulfonic acid co-ligands on the silica surface, which resulted in greatly reduced retention times. Acting as immobilized counterions, they allowed to reduce the concentration of counterions in the mobile phase, which is favorable for liquid chromatography (LC)-electrospray ionization-mass spectrometry application. Ibuprofen showed a single peak under ambient column temperature. However, application of cryogenic cooling of the column enabled to achieve baseline separation at -20°C column temperature. It can be explained by an enthalpically dominated separation, which leads to an increase in separation factors when the temperature is reduced. While it is quite uncommon to work at subzero degree column temperature, this work illustrates the potential to exploit such temperature regime for optimization of LC enantiomer separations. Chemistry ; Enantiomer ; Hydroxymethyl ; Counterion ; Enantioselective synthesis ; Chiral column chromatography ; Silica gel ; Chromatography ; Quinidine ; Polymer ; Polymer chemistry ; Organic chemistry ; Ion ; Catalysis Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Mass Spectrometry Techniques and Applications
Authors: Manuel Díaz de los Ríos | Evelyn Faife Pérez
Source: Research Square (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Use of Group Contribution Methods, Hansen’s Theory of Solubility and Microsoft Excel in the Selection of Solvents for the Extraction of Natural Products Abstract Usually, Hansen's theory and the COSMO continuous solvation models have been used for the selection of solvents related to a solute, using the HSPiP and COSMO-RS computational tools, respectively. Those tools are not always available to many researchers; for this reason, Microsoft Excel has been used for the estimation of good solvents in the extraction of natural products of high value and interesting for the food, pharmaceutical and cosmetic industries. This method is applicable to cases where there is little, or practically no information about the compound of interest, for which functional group contribution methods are used, among them, the Joback method for the estimation of properties necessary for the determination of solubility models, and Van Krevelen method for the initial estimation of the Hansen solubility parameters. For solubility estimates, the Flory-Hugging model is used, from which and its classification, according to the North American Pharmacopeia, the good and bad solvents are defined in the selected database for the final estimation of Hansen's parameters and the solubility region. The methodology was validated with ten study cases reported in the literature. Solubility ; Hildebrand solubility parameter ; Microsoft excel ; Extraction (chemistry) ; Selection (genetic algorithm) ; Group contribution method ; Solvation ; Computer science ; Chemistry ; Thermodynamics ; Mathematics ; Chromatography ; Organic chemistry ; Artificial intelligence ; Phase equilibrium ; Solvent ; Physics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Chemical and Physical Properties in Aqueous Solutions
Authors: Валерій Лавріненко | Володимир Федорович | Євгеній Островерх | Володимир Солод
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2023-05-25)
Field: Chemistry — Spectroscopy

СУЧАСНІ РОЗРОБКИ ЩОДО СПРЯМОВАНОГО ВПЛИВУ НА РІЗАЛЬНУ ПОВЕРХНЮ АЛМАЗНО-АБРАЗИВНОГО ІНСТРУМЕНТУ ТА ЙОГО КОНТАКТНУ ЗОНУ В ПРОЦЕСАХ МЕХАНІЧНОЇ ОБРОБКИ (ОГЛЯД) This article provides information on modern developments in the direction of directed impact on the cutting surface of a diamond-abrasive tool and its contact zone in machining processes. For the most part, such processing is faced with issues of influence on the cutting surface of diamond tools, including ruling mechanical and electrophysical, taking into account the defectiveness of the diamonds that undergo processing, the directed influence on the surface of such diamonds, thermal and modification of the surface of diamonds. Such developments make it possible to significantly intensify processing processes and increase the efficiency of the diamond abrasive tool. That is why, in this review, the main attention is paid to the presentation of modern developments, known from scientific publications, mainly for the last 5 years, related to the above-mentioned issues. Machining ; Abrasive ; Diamond ; Abrasive machining ; Diamond tool ; Presentation (obstetrics) Adsorption, diffusion, and thermodynamic properties of materials ; Diamond and Carbon-based Materials Research ; Tunneling and Rock Mechanics
Authors: Yaxin Xu | Guoqing Sun | Zijing Wan | Dongwei Xu | Xiaobing Luo
Source: Physica Scripta (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Theoretical study on the thermal transport and its tunability of a-plane trilayer GaN Abstract Two-dimensional (2D) a-plane gallium nitride, a non-layered 2D material, has promising applications in photoelectric nanodevices due to its direct band bandgap. Herein, employing molecular dynamics simulations, we studied the thermal transport properties of a-plane trilayer GaN, and the temperature, together with strain modulation on the thermal conductivity of the system. The a-plane trilayer GaN shows anisotropic thermal conductivity with 70.22 Wm −1 K −1 and 41.81 Wm −1 K −1 along zigzag- and armchair- directions respectively at room temperature when extrapolated to infinite size. In addition, the thermal conductivity of trilayer GaN exhibits decreasing trend in response to the increase of temperature. The thermal conductivity decreases monotonically with the increased compressive uniaxial and biaxial strain, while it shows an up-then-down trend under tensile strain. The tunability of thermal conductivity under biaxial strain is much larger than that of uniaxial strain. The phonon density of states is further investigated to understand the behavior of thermal conductivity. The tunability of the system thermal conductivity will expand its applications in thermal management and nanodevices. Materials science ; Thermal conductivity ; Zigzag ; Gallium nitride ; Condensed matter physics ; Anisotropy ; Phonon ; Thermal ; Composite material ; Optics ; Thermodynamics Thermal properties of materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Fabrice Roncoroni | Ana Sanz-Matı́as | Siddharth Sundararaman | David Prendergast
Source: arXiv (Cornell University) (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

Unsupervised learning of representative local atomic arrangements in molecular dynamics data Molecular dynamics (MD) simulations present a data-mining challenge, given that they can generate a considerable amount of data but often rely on limited or biased human interpretation to examine their information content. By not asking the right questions of MD data we may miss critical information hidden within it. We combine dimensionality reduction (UMAP) and unsupervised hierarchical clustering (HDBSCAN) to quantitatively characterize prevalent coordination environments of chemical species within MD data. By focusing on local coordination, we significantly reduce the amount of data to be analyzed by extracting all distinct molecular formulas within a given coordination sphere. We then efficiently combine UMAP and HDBSCAN with alignment or shape-matching algorithms to partition these formulas into structural isomer families indicating their relative populations. The method was employed to reveal details of cation coordination in electrolytes based on molecular liquids. Computer science ; Cluster analysis ; Dimensionality reduction ; Data mining ; Molecular dynamics ; Matching (statistics) ; Artificial intelligence ; Chemistry ; Mathematics ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Ramzi Jalgham
Source: مجلة جامعة بني وليد للعلوم الإنسانية والتطبيقية (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Carbozone Compound Corrosion Inhibition Efficiency in Relation to Quantum Chemical Parameters: A QSAR Study for Predictive Models This article discusses the use of carbozones compounds as corrosion inhibitors and the correlation between their quantum chemical parameters and molecular dynamic parameters with their corrosion inhibition efficiency. The results show that the use of a composite index of quantum parameters can accurately describe the corrosion inhibitory performance of carbozone molecules. A predictive QSAR model can be developed using the study's statistical methodologies to predict corrosion inhibitory behavior of organic compounds and generate new corrosion inhibitors. The inclusion of adsorption energy in the nonlinear equation assist decrease the number of descriptors utilized in the model. Overall, Researchers should focus more on these criteria in future research since they have the potential to enhance results. Quantitative structure–activity relationship ; Corrosion ; Quantum chemical ; Molecular descriptor ; Quantum ; Biological system ; Computational chemistry ; Molecule ; Nonlinear system ; Chemistry ; Materials science ; Computer science ; Stereochemistry ; Organic chemistry ; Physics ; Quantum mechanics Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants