jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianzhong Xiong | Wenhua Ye
Source: Preprints.org (preprint, 2023-05-08)
Field: Materials Science — Surfaces, Coatings and Films

Context-Based Synthesis of Unmeasurable Signals before Attenuation in Line-Scan X-ray Transmission Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals. Attenuation ; SIGNAL (programming language) ; Energy (signal processing) ; Context (archaeology) ; Logarithm ; Optics ; Transmission (telecommunications) ; Line (geometry) ; Computer science ; Acoustics ; Algorithm ; Mathematics ; Physics ; Mathematical analysis ; Geometry ; Telecommunications ; Geology ; Statistics Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging ; Machine Learning in Materials Science
Authors: Ruiwen Xie | Yixuan Zhang | Fu Li | Zhiyuan Li | Hongbin Zhang
Source: TUbilio (Technical University of Darmstadt) (preprint, 2023-09-08)
Field: Materials Science — Materials Chemistry

Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10$^{-3}$ $Ω$ cm)$^{-1}$) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space. Bayesian optimization ; Spin (aerodynamics) ; Skew ; Scattering ; Conductivity ; Condensed matter physics ; Materials science ; Work (physics) ; Work function ; Space (punctuation) ; Computer science ; Physics ; Nanotechnology ; Thermodynamics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: U. D George | M. G Akpan | D. N Onwe
Source: FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY (article, 2023-05-31)
Field: Chemistry — Analytical Chemistry

MACHINE LEARNING CLASSIFIER FOR DALIUM GUINEENSE FRUIT USING ITS PHYSICAL PROPERTIES Dalium guineense (DG) is a wild fruit with a brittle epicarp that may be broken accidentally or intentionally while processing during any of the unit operations thereby creating a binomial mixture. Having a binomial mixture of similar items that need to be separated for processing or storage purposes presents a common challenge. This research aims at selecting an appropriate machine learning classifier for the classification of DG fruits. Fifteen measured physical characteristics of randomly selected 200 DG fruits were obtained. Fifty percent of the fruits were deshelled, while the remaining 50% were whole fruits. Different machine learning classifiers were chosen from Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) classification models, using the classification learner toolkit of MATLAB. The results revealed that Coarse Gaussian SVM and Cosine KNN presented an outstanding classification accuracy of 98.5% compared to other classifiers under investigation. The two classifiers also attained precision, sensitivity, specificity, and F-scores of 99.0%, 98.0%, 99.0%, and 98.5% respectively. The method deployed in this study demonstrates superiority to those reported in some literature. This research recommends the adoption of either the coarse Gaussian SVM or the cosine KNN as the most appropriate classifier for the DG fruits classification. Support vector machine ; Classifier (UML) ; Decision tree ; Pattern recognition (psychology) ; Random forest ; Random subspace method Spectroscopy and Chemometric Analyses ; African Botany and Ecology Studies ; Food Drying and Modeling
Authors: John Kendrick | Andrew D. Burnett
Source: ChemRxiv (preprint, 2023-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the Stability and Disorder in the Polymorphs of L-Cysteine through Density Functional Theory and Vibrational Spectroscopy Static and dynamic density functional calculations are reported for the four known polymorphs of L-Cysteine. Static calculations are used to explore the relative free energies (within the harmonic approximation) of the polymorphs as a function of pressure. An important feature of the structural differences between the polymorphs is shown to be the dihedral angle of the C-C-S-H bond. It is shown that by varying this angle it is possible to move between hydrogen bonding motifs S-H...S and S-H...O in all four polymorphs. The energetics for dihedral angle rotation are explored and the barriers for rotation between the hydrogen bonding motifs have been calculated for each polymorph. Two possible models for the experimental disorder observed in Form I at room temperature are explored using both static and dynamic methods; a domain disorder model, where the disorder is localised, and a dispersed disorder model, where the disorder is randomly distributed throughout the crystal. Molecular dynamics calculations show transitions between the two hydrogen bonding motifs occurring in the dispersed disorder model at 300 K and 350 K. In addition, molecular dynamics calculations of Form IV also showed the onset of hydrogen bond disorder at 300 K. Calculations of the predicted infrared and terahertz absorption are performed for both the static and dynamic simulations and the results compared with experiment to understand the influence of disorder on the observed spectra. Dihedral angle ; Hydrogen bond ; Density functional theory ; Molecular dynamics ; Infrared spectroscopy ; Chemistry ; Crystallography ; Molecular vibration ; Materials science ; Computational chemistry ; Molecule Nonlinear Optical Materials Research ; Solid-state spectroscopy and crystallography ; Molecular Spectroscopy and Structure
Authors: Wen-Jing Ai | Jing Li | Dongsheng Cao | Shao Liu | Yi-Yun Yuan | Yan Li | Gui‐Shan Tan | Kang Xu | Xia Yu | Fenghua Kang | Zhen‐Xing Zou | Wen‐Xuan Wang
Source: ChemRxiv (preprint, 2023-06-05)
Field: Chemistry — Spectroscopy

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculation with Density Functional Theory Level Performance for Structure Assignment Nuclear magnetic resonance (NMR) chemical shift calculation is a powerful tool for structural elucidation, and has been extensively employed in both synthetic and natural product chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks. Herein, we have constructed a 54-layer deep graph convolutional network for 13C NMR chemical shift calculation, which achieved high accuracy with low time-cost, and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semi-empirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K rings junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculation to date. This model has been developed into a user-friendly software, providing a useful tool for routinary rapid structure validation and assignation, as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculation. Chemical shift ; Density functional theory ; Carbon-13 NMR ; Computer science ; Graph theory ; Graph ; Computational chemistry ; Chemistry ; Theoretical computer science ; Mathematics ; Physical chemistry ; Organic chemistry Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Abigail Chiu | Mehdi Rahimi | Yeongjoon Lee | Huong Q. Nguyen | Woonghee Lee
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

A toolset for the solid-state NMR-based 3D structure calculations of proteins Solid-state ; Solid-state nuclear magnetic resonance ; Chemistry ; State (computer science) ; Crystallography ; Computational chemistry ; Computer science ; Chemical physics ; Nuclear magnetic resonance ; Physical chemistry ; Physics ; Programming language Advanced NMR Techniques and Applications ; Nuclear Physics and Applications ; Enzyme Structure and Function
Authors: Ehsan Moradpur‐Tari | Sergei Vlassov | Sven Oras | Mart Ernits | Elyad Damerchi | Andreas Kyritsakis | Vahur Zadin
Source: Research Square (preprint, 2023-07-26)
Field: Materials Science — Materials Chemistry

Nano1D: An accurate Computer Vision model for segmentation and analysis of low-dimensional objects Abstract Microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional irregular and deformable objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on Ag nanowires, and successfully segments and analyzes their geometrical characteristics including length, thickness, perimeter and distributions. The function of the algorithm is not undermined by the size, number, density, orientation and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Nano1D can analyze 1D nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc. Segmentation ; Preprocessor ; Artificial intelligence ; Orientation (vector space) ; Computer science ; Computer vision ; Nanowire ; Perimeter ; Function (biology) ; Microscopy ; Pattern recognition (psychology) ; Geometry ; Materials science ; Mathematics ; Optics ; Physics ; Nanotechnology Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Manuel Ballester | E. Márquez | Almudena P. Márquez | Santiago López-Tapia | Oliver Cossairt | Florian Willomitzer | Aggelos K. Katsaggelos
Source:  (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Deep Learning Method to Find Swanepoel Envelopes in Transmission Spectroscopy This research presents a novel Deep Learning method to find the Swanepoel Envelopes from the transmission spectra of dielectric thin films. This method improves the efficiency and accuracy of the film's optical characterization. Characterization (materials science) ; Spectroscopy ; Transmission (telecommunications) ; Materials science ; Dielectric ; Artificial intelligence ; Computer science ; Biological system ; Nanotechnology ; Optoelectronics ; Telecommunications ; Physics ; Biology Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies ; Liquid Crystal Research Advancements
Authors: Noureddine Boumdouha | Mohamed Abderaouf Louar
Source: Journal of Basic & Applied Sciences (article, 2023-08-18)
Field: Materials Science — Polymers and Plastics

Influence of Microstructure on the Dynamic Behaviour of Polyurethane Foam with Various Densities Polyurethane foam is reinforced with varying proportions of metal loads and other components to increase shock absorption and mechanical impact. The main objective is to develop high-performance polymeric materials based on polyurethane foam developed with different compositions and specific densities. We monitor the growth distances and temperatures of the polyurethane foam in time to reach the optimum formulations. We conduct static compression tests and investigate the effect of drop weight on the deformation of polyurethane foam structures by dropping a weight from a specified height. Dynamic collisions cause deformations of the polyurethane foam structure. After investigating the low weight, we found that polyurethane foams have a good absorption coefficient at certain frequencies. Dynamic stress-strain response curves are used to characterize different stress rates. High-stress levels and similar strains indicate a high resistance to shock. We follow the evolution of microstructure structures by scanning electron microscopy (SEM) to observe deformation and fracture behavior with reversibility and recovery. Polyurethane ; Materials science ; Composite material ; Microstructure ; Scanning electron microscope ; Deformation (meteorology) ; Compression (physics) Polymer composites and self-healing ; Supramolecular Chemistry and Complexes ; Cellular and Composite Structures
Authors: Aleksei Kaliuzhnyi
Source: Artificial Intelligence Evolution (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Application of Model Data for Training the Classifier of Defects in Rail Bolt Holes in Ultrasonic Diagnostics The task of searching for defects on defectograms of ultrasonic inspection of rails using machine learning methods is considered difficult due to the lack of a sufficient representative training data set. In this study, on the example of defects in bolt holes of rails, the possibility of synthesizing an effective classifier trained on artificial data obtained by mathematical modeling is shown. Experiments have shown a high accuracy of predicting samples of real (not model) data exceeding 99%. Classifier (UML) ; Training set ; Ultrasonic sensor ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Acoustics Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Kaitlin McCardle
Source: Nature Computational Science (article, 2023-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Collaborations drive energy storage research Dr Y. Shirley Meng, Professor of Molecular Engineering at the University of Chicago and Chief Scientist at the Argonne Collaborative Center for Energy Storage Science (ACCESS), discusses her research on energy storage materials and the importance of multidisciplinary collaborations. National laboratory ; Multidisciplinary approach ; Research center ; Energy storage ; Center (category theory) ; Science and engineering ; Engineering physics ; Engineering ; Library science ; Engineering ethics ; Computer science ; Engineering management ; Physics ; Sociology ; Political science ; Chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Catalysis for Biomass Conversion
Authors: Rainer Niekamp | Johanna Niemann | Maximilian Reichel | Jörg Schröder
Source: PAMM (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

A data generation approach for surrogate models for magneto‐static simulations Abstract In this paper, we propose an efficient numerical scheme for the prediction of the magnetic stray fields in two‐dimensional random microheterogeneous materials. Since data‐driven models require thousands of training datasets, Finite Element Method simulations appear to be too time consuming. Therefore, a stochastic model based on Brownian motion, which uses an efficient evaluation of stochastic transition matrices, is used as a Poisson solver to generate training data. Solver ; Computer science ; Finite element method ; Magneto ; Poisson distribution ; Applied mathematics ; Brownian motion ; Algorithm ; Scheme (mathematics) ; Statistical physics ; Mathematical optimization ; Computational science ; Mathematics ; Physics ; Engineering ; Mathematical analysis ; Mechanical engineering ; Statistics ; Magnet Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Edward Linscott | Nicola Colonna | Riccardo De Gennaro | Ngoc Linh Nguyen | Giovanni Borghi | Andrea Ferretti | Ismaïla Dabo | Nicola Marzari
Source: arXiv (Cornell University) (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

koopmans: an open-source package for accurately and efficiently predicting spectral properties with Koopmans functionals Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity condition to the entire electronic manifold, ensuring that orbital energies match the corresponding electron removal/addition energy differences (in contrast to semi-local DFT, where a mismatch between the two lies at the heart of the band gap problem and, more generally, the unreliability of Kohn-Sham orbital energies). This strategy has proven to be very powerful, yielding molecular orbital energies and solid-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost while preserving a functional formulation. This paper reviews the theory of Koopmans functionals, discusses the algorithms necessary for their implementation, and introduces koopmans, an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations and obtain reliable spectral properties of molecules and materials. Density functional theory ; Hybrid functional ; Perturbation theory (quantum mechanics) ; Workflow ; Computer science ; Band gap ; Statistical physics ; Piecewise ; Physics ; Algorithm ; Computational physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Chemical Physics Studies
Authors: Henrik Christiansen | Federico Errica | Francesco Alesiani
Source: arXiv (Cornell University) (preprint, 2023-09-24)
Field: Materials Science — Materials Chemistry

Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling The performance of Hamiltonian Monte Carlo simulations crucially depends on both the integration timestep and the number of integration steps. We present an adaptive general-purpose framework to automatically tune such parameters, based on a local loss function which promotes the fast exploration of phase-space. We show that a good correspondence between loss and autocorrelation time can be established, allowing for gradient-based optimization using a fully-differentiable set-up. The loss is constructed in such a way that it also allows for gradient-driven learning of a distribution over the number of integration steps. Our approach is demonstrated for the one-dimensional harmonic oscillator and alanine dipeptide, a small protein common as a test case for simulation methods. Through the application to the harmonic oscillator, we highlight the importance of not using a fixed timestep to avoid a rugged loss surface with many local minima, otherwise trapping the optimization. In the case of alanine dipeptide, by tuning the only free parameter of our loss definition, we find a good correspondence between it and the autocorrelation times, resulting in a $&gt;100$ fold speed up in optimization of simulation parameters compared to a grid-search. For this system, we also extend the integrator to allow for atom-dependent timesteps, providing a further reduction of $25\%$ in autocorrelation times. Maxima and minima ; Monte Carlo method ; Autocorrelation ; Integrator ; Hybrid Monte Carlo ; Harmonic oscillator ; Statistical physics ; Hamiltonian (control theory) ; Computer science ; Parameter space ; Applied mathematics ; Mathematical optimization ; Mathematics ; Physics ; Mathematical analysis ; Markov chain Monte Carlo ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: G. I. Makarov | T. M. Makarova
Source: Физика и химия стекла (article, 2023-11-01)
Field: Materials Science — Ceramics and Composites

Molecular Dynamics Modeling of the Grade E Borosilicate Glass Structure Using a Crystal Structural Template A new method for molecular dynamics (MD) modeling of the glass structure using a crystal structural template is proposed. The template is based on the unit cell of the crystalline phase, whose composition is qualitatively similar to the modeled glass. Using this approach and multistage MD simulation, the model of the spatial structure of grade E borosilicate glass, reproducing its physicochemical characteristics, is obtained. The proposed method enables to model the glass structure using classical MD methods with greater productivity and stability. Borosilicate glass ; Materials science ; Molecular dynamics ; Dynamics (music) ; Crystal structure ; Crystal (programming language) ; Crystallography ; Composite material ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Computational chemistry Glass properties and applications ; Pigment Synthesis and Properties ; Recycling and utilization of industrial and municipal waste in materials production