jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shao-Chun Lee | Yang Zhang
Source: The Journal of Chemical Physics (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent β in a model tetrahedral liquid ZnCl2 from Tm + 85 K to Tm + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl2. We found a non-monotonic temperature dependence of β from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. This result may contribute to new understandings of the structural relaxation of other network liquids. Tetrahedron ; Supercooling ; Dynamical heterogeneity ; Chemical physics ; Relaxation (psychology) ; Molecular dynamics ; Materials science ; Structural change ; Fusion ; Dynamic relaxation ; Exponent ; Thermodynamics ; Crystallography ; Chemistry ; Glass transition ; Polymer ; Computational chemistry ; Physics Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Abdul Momin | Chennoju Sai Rohith | Gattamaneni Pranav | Puchalapalli Sathwik | Suryakanth V. Gangashetty
Source:  (article, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Detection of Milk Quality Utilizing Fourier Transform Infrared Spectroscopy Ensuring consumer safety and maintaining milk quality are of utmost importance in the dairy industry. A recent research initiative introduces an innovative milk detection system that leverages Fourier Transform Infrared (FTIR) spectroscopy. This cutting-edge system utilizes FTIR analysis to evaluate the chemical composition of milk samples, enabling precise determination of their quality. By incorporating advanced machine learning algorithms for data interpretation, the system categorizes milk samples into distinct quality grades, effectively identifying adulteration, contamination, and compositional alterations. The implementation of this system holds the potential of attaining substantial improvements in milk quality control protocols, thereby safeguarding consumer health and enhancing overall confidence in milk products. Fourier transform infrared spectroscopy ; Fourier transform spectroscopy ; Fourier transform ; Infrared ; Infrared spectroscopy ; Quality (philosophy) ; Spectroscopy ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Optics ; Physics ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Claudia Ardino | Filomena Sannio | Giulio Poli | Salvatore Galati | Elena Dreassi | Lorenzo Botta | Jean‐Denis Docquier | Ilaria D’Agostino
Source: European Journal of Medicinal Chemistry (article, 2024-03-28)
Field: Chemistry — Organic Chemistry

An update on antibacterial AlkylGuanidino Ureas: Design of new derivatives, synergism with colistin and data analysis of the whole library Colistin ; Antimicrobial ; Bacteria ; Chemistry ; Antibiotic resistance ; Antibacterial activity ; Gram-negative bacteria ; Antibiotics ; Combinatorial chemistry ; Microbiology ; Biochemistry ; Escherichia coli ; Biology ; Organic chemistry ; Gene ; Genetics Antimicrobial agents and applications ; Phenothiazines and Benzothiazines Synthesis and Activities ; Antimicrobial Peptides and Activities
Authors: Amarin Chanpariyavatevong | Chamroeun Se | Anantaya Timtong | Wanwanut Boongsood | Attasit Wiangkham
Source: Journal of Materials Engineering and Performance (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Investigating Cryogenic and Heat Treatment Effects on Hardness and Wear of Uncoated Carbide Inserts: A Microstructure and AI Approach Cryogenic treatment ; Tempering ; Materials science ; Indentation hardness ; Carbide ; Tungsten carbide ; Microstructure ; Metallurgy ; High-speed steel Metal Alloys Wear and Properties ; Advanced materials and composites ; Advanced machining processes and optimization
Authors: Cong Hou | Yibo Sun | Yuhang Lu | Zimo Li | Jun Ni
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Regulating magnetic skyrmions in multiferroic monolayer MnOBr Two-dimensional multiferroic materials that exhibit both ferroelectricity and ferromagnetism provide a new platform for the discovery and regulation of magnetic skyrmions. In this study, we utilize first-principles calculations and Monte Carlo simulations to explore the properties and regulation of magnetic skyrmions in a novel multiferroic monolayer, MnOBr. MnOBr exhibits skyrmions without the need for an external magnetic field. Upon applying an external magnetic field, we found the disappearance of labyrinth domains and the formation of a periodic arrangement of the skyrmion lattice. By employing machine learning techniques, we depict a phase diagram of MnOBr under varying magnetic fields and biaxial strain, which provides a detailed depiction of phase transitions of spin textures in monolayer MnOBr. Furthermore, in MnOBr/CdClBr heterostructures, we demonstrate that the creation and annihilation of magnetic skyrmions can be controlled by switching the polarization direction of the Janus CdClBr. These findings show potential applications of MnOBr as a 2D magnetic skyrmion material in spintronic devices. Multiferroics ; Skyrmion ; Monolayer ; Condensed matter physics ; Materials science ; Nanotechnology ; Physics ; Ferroelectricity ; Optoelectronics Multiferroics and related materials ; 2D Materials and Applications ; Magnetic properties of thin films
Authors: Elloise Coyle | Mickaël Leclercq | Clarisse Gotti | Florence Roux‐Dalvai | Arnaud Droit
Source: Journal of Proteome Research (article, 2024-12-07)
Field: Chemistry — Spectroscopy

ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide. This model was trained on a manually labeled data set and subsequently validated to assess its predictive accuracy. The results demonstrate that the model reliably identifies peptide peaks in the presence of noise, achieving a Matthews correlation coefficient (MCC) of 0.81 on an independent test data set, surpassing mProphet's MCC of 0.71. Implemented in R as ProPickML, this tool offers a competitive, cost-effective alternative to existing techniques, significantly reducing reliance on isotopic labeling and enhancing the accuracy of peptide identification in SRM workflows. Proteomics ; Computational biology ; Chemistry ; Computer science ; Nanotechnology ; Biology ; Materials science ; Biochemistry Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Danam Mahesh | N. V. Raju | Snigdha Sen
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2024-11-22)
Field: Materials Science — Polymers and Plastics

Machine learning approaches for predicting dielectric properties of banana fibers reinforced with polypropylene composites Composite material ; Polypropylene ; Materials science ; Dielectric Natural Fiber Reinforced Composites ; Infrastructure Maintenance and Monitoring ; Smart Materials for Construction
Authors: Po-Yen Chen | Kiyou Shibata | Katsumi Hagita | Tomohiro Miyata | Teruyasu Mizoguchi
Source: Micron (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Predicting ELNES/XANES spectra by machine learning with an atomic coordinate-independent descriptor and its application to ground-state electronic structures XANES ; Ground state ; Spectral line ; Electronic structure ; Materials science ; Atomic physics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Jun-Nan Liang | Hua Tong | Yu‐Jia Zeng | Wu‐Xing Zhou
Source: Journal of Physics Condensed Matter (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Machine learning assisted understanding of the layer-thickness dependent thermal conductivity in fluorinated graphene Manipulating thermal conductivity (<i>κ</i>) plays vital role in high-performance thermoelectric conversion, thermal insulation and thermal management devices. In this work, we using the machine learning-based interatomic potential and the phonon Boltzmann transport equation to systematically investigate layer thickness dependent<i>κ</i>of fluorinated graphene (FG). We show that the lattice<i>κ</i>of FG can be significantly decreased with Bernal bilayer stacking. Surprisingly, the further increasing of stacking layer can no longer affect the<i>κ</i>, however, the<i>κ</i>is increased in the bulk configuration. The variation of<i>κ</i>can be attributed to the crystal symmetry change from P-3m1 (164) at single layer to P3m1 (156) at multilayer. The decreasing crystal symmetry from single layer to bilayer resulting stronger phonon scattering and thus leading a lower<i>κ</i>. Moreover, we also show that the contribution of acoustic mode to<i>κ</i>decreases with the increase of layers, while the contribution of optical mode to<i>κ</i>is increased with increasing layers. These results provide a further understanding for the phonon scattering mechanism of layer thickness dependent<i>κ</i>. Thermal conductivity ; Materials science ; Phonon ; Stacking ; Graphene ; Condensed matter physics ; Scattering ; Phonon scattering ; Bilayer ; Bilayer graphene ; Boltzmann equation ; Thermoelectric materials ; Thermoelectric effect ; Layer (electronics) ; Composite material ; Nanotechnology ; Optics ; Thermodynamics ; Chemistry ; Nuclear magnetic resonance ; Membrane ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Wenqiu Shang | Tao Hu | Ding Li | Shichang Li | Xianju Zhou | Chunbao Feng | Dengfeng Li
Source: Journal of Electronic Materials (article, 2024-06-05)
Field: Materials Science — Materials Chemistry

Combined First-Principles and Machine Learning Study of Thermal Transport and Thermoelectric Properties of p-type Halide Perovskite CsCdX3 (X = Cl, Br) Halide ; Perovskite (structure) ; Thermoelectric effect ; Solid-state physics ; Thermal ; Materials science ; Thermoelectric materials ; Chemistry ; Condensed matter physics ; Inorganic chemistry ; Crystallography ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Valigun Margarita | CEO, Nova Ceiling design, Orlando, USA
Source: The American Journal of Engineering And Technology (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

MODERN APPROACHES TO THE USE OF COMPOSITE MATERIALS IN CONSTRUCTION A review of the literature on scientific approaches in the development of composite materials and building structures made of composites is carried out. When creating and manufacturing traditional and new composite materials, for example, by additive manufacturing, and when creating structures and structures in engineering calculations, new techniques, finite element computational software systems, and neural network technologies are used, which are used in the creation of modern metal and composite materials, analysis of mechanical characteristics of materials, forecasting loads on the structure, optimization of structures and calculation of their construction characteristics. The distinctive features of modern composite materials are shown. The main types of composite materials are considered: talc, diatomite, calcium carbonate, gibbsite, barium sulfate, feldspar, nepheline, aragonite, calcium carbonate, wool, silk, cotton, linen, jute, wood pulp, asbestos, fiberglass, metal fibers, quartz fibers, basalt fibers, polyamide fibers, polyester fibers, polyvinyl alcohol fibers, carbon fibers, viscose fibers. The physical and mechanical characteristics of composite materials (based on epoxy, aluminum, carbon, magnesium, and nickel matrices) and traditional (steel, aluminum, brick, concrete) building materials are presented. The disadvantages of such composite materials as carbon fiber, fiberglass, organoplastics, textolite, carbon concrete, and polystyrene concrete are presented. Deformation diagrams of some types of fibers for composite materials are considered: high-modulus carbon fibers, high-strength carbon fibers, aramid fibers, glass fibers, and basalt fibers. The advantages of the system of external reinforcement of building structures with composite materials are described. Examples of reinforcement of building structures are considered: reinforced concrete reinforcement; reinforcement of floor slabs; reinforcement of columns; and reinforcement of brick walls. Materials science ; Composite material ; Composite number ; Basalt fiber ; Specific strength ; Fiber Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Material Properties and Applications
Authors: K. Nasri | Lotfi Toubal
Source: Preprints.org (preprint, 2024-01-29)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Approach for Assessing mechanical properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation Amidst escalating environmental concerns, short natural fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% about the impact load, absorbed energy, and tensile strength, respectively. Composite material ; Materials science ; Natural fiber ; Artificial neural network ; Fiber ; Radiation ; Natural (archaeology) ; Computer science ; Artificial intelligence ; Optics ; Physics Natural Fiber Reinforced Composites
Authors: R.I. Zubatyuk | Małgorzata Biczysko | Kavindri Ranasinghe | Nigel W. Moriarty | Hatice Gökcan | Holger Kruse | Billy K. Poon | Paul D. Adams | Mark P. Waller | Adrián E. Roitberg | Olexandr Isayev | Pavel V. Afonine
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-21)
Field: Materials Science — Materials Chemistry

AQuaRef: Machine learning accelerated quantum refinement of protein structures Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL. Computer science ; Quantum ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: J. D. Langcaster | Daniel S. Balint | M.R. Wenman
Source: Mechanics of Materials (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Adapting U-Net for linear elastic stress estimation in polycrystal Zr microstructures Materials science ; Convolutional neural network ; Finite element method ; Crystallite ; Artificial neural network ; Microstructure ; Linear elasticity ; Texture (cosmology) ; Algorithm ; Structural engineering ; Computational science ; Composite material ; Artificial intelligence ; Computer science ; Image (mathematics) ; Metallurgy ; Engineering Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies
Authors: S.M. Liu | Zhen-Shuai Lei | Meng-Ru Chen | Ting Song | Zi‐Jiang Liu | Xiao Wei Sun
Source: The Journal of Physical Chemistry A (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Investigation on Melting Curves and Phase Diagrams for CaO<sub>3</sub> Using Deep Learning Potentials Melting in the deep rocky parts of planets plays an important role in geological processes such as planet formation, seismicity, magnetic field generation, thermal convection, and crustal evolution. Such processes are the key way to understanding the dynamics of planetary interiors and the history as well as mechanisms of planetary evolution. We herein investigate the melting curves and pressure-temperature (<i>P</i>-<i>T</i>)-phase diagrams for CaO<sub>3</sub>, a candidate mineral for the lower mantle, by means of the deep learning potential model. Using first-principles, molecular dynamics, and quasi-harmonic approximation, the reliability of the deep learning potential model is verified by calculating the high-temperature and high-pressure equations of state and phase transition pressures for the orthorhombic and tetragonal structures of CaO<sub>3</sub> described by space groups <i>Aea</i>2 and <i>P</i>4̅2<sub>1</sub><i>m</i>, respectively. The melting temperatures of 975, 850, and 755 K at zero pressure are obtained by the single-phase, void, and two-phase methods, respectively, and their melting temperatures are analyzed by the radial distribution function and mean-square displacement to analyze the melting process. Finally, the melting phase diagrams of CaO<sub>3</sub> at 0-135 GPa were obtained by the two-phase method. Phase diagram ; Phase (matter) ; Learning curve ; Materials science ; Geology ; Mineralogy ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Deperias Kerre | Anne Laurent | Kenneth Maussang | Dickson Odhiambo Owuor
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

An Instruction Dataset for Extracting Quantum Cascade Laser Properties from Scientific Text Cascade ; Quantum cascade laser ; Quantum ; Computer science ; Physics ; Chemistry ; Quantum mechanics ; Chromatography Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Text Analysis Techniques
Authors: Adrien Teurtrie | Nathanaël Perraudin | Thomas Holvoet | Hui Chen | Duncan T. L. Alexander | Guillaume Obozinski | C. Hébert
Source: Machine Learning Science and Technology (article, 2024-11-12)
Field: Chemistry — Spectroscopy

From STEM-EDXS data to phase separation and quantification using physics-guided NMF Abstract We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of x-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables , which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized. Non-negative matrix factorization ; Separation (statistics) ; Phase (matter) ; Physics ; Computer science ; Machine learning ; Matrix decomposition Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Nuclear Physics and Applications