jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aris Karnezis | Artur L. Gower
Source: arXiv (Cornell University) (preprint, 2024-01-17)
Field: Chemistry — Physical and Theoretical Chemistry

Calculating pair-correlations from random particle configurations Particle pair-correlations are broadly used to describe particle distributions in chemistry, physics, and material science. Many theoretical methods require the pair-correlation to predict material properties such as fluid flow, thermal properties, or wave propagation. In all these applications it is either important to calculate a pair-correlation from specific particle configurations, or vice-versa: determine the likely particle configurations from a pair-correlation which is needed to fabricate a particulate material. Most available methods to calculate the pair-correlation from a particle configuration require that the configuration be very large to avoid effects from the boundary. Here we show how to avoid boundary effects even for small particle configurations. Having small particle configurations leads to far more efficient numerical methods. We also demonstrate how to use techniques from smooth nonlinear optimisation to quickly recover a particle configuration from a pair-correlation. Particle (ecology) ; Statistical physics ; Nonlinear system ; Boundary (topology) ; Pair potential ; Boundary value problem ; Physics ; Mathematics ; Mathematical analysis ; Quantum mechanics Electrostatics and Colloid Interactions ; Material Dynamics and Properties ; Granular flow and fluidized beds
Authors: Martin Sommer-Jörgensen | Stefan Goedecker
Source: The Journal of Chemical Physics (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

COMPASS: Double-ended saddle point search as a constrained optimization problem We present an algorithm to find first order saddle points on the potential energy surface (PES). The algorithm is formulated as a constrained optimization problem that involves two sets of atomic coordinates (images), a time-varying distance constraint and a constraint on the energy difference. Both images start in different valleys of the PES and are pulled toward each other by gradually reducing the distance. The search space is restricted to the pairs of configurations that share the same potential energy. By minimizing the energy while the distance shrinks, a minimum of the constrained search space is tracked. In simple cases, the two images are confined to their respective sides of the barrier until they finally converge near the saddle point. If one image accidentally crosses the barrier, the path is split at suitable locations and the algorithm is repeated recursively. The optimization is implemented as a combination of a quasi-Newton optimization and a linear constraint. The method was tested on a set of Lennard-Jones-38 cluster transitions and a set of 121 molecular reactions using density functional theory calculations. The efficiency in terms of energy and force evaluation is better than with competing methods as long as they do not switch to single-ended methods. The construction of a continuous search path with small steps and the ability to focus on arbitrary subsegments of the path provide an additional value in terms of robustness and flexibility. Saddle point ; Mathematical optimization ; Constraint (computer-aided design) ; Robustness (evolution) ; Path (computing) ; Saddle ; Algorithm ; Mathematics ; Energy minimization ; Energy functional ; Computer science ; Physics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Maryam Azizi | Francisco A. Delesma | Matteo Giantomassi | Davis Zavickis | Mikael Kuisma | Kristian Thyghesen | Dorothea Golze | Alexander Buccheri | Mengxiu Zhang | Patrick Rinke | Claudia Draxl | Andris Guļāns | Xavier Gonze
Source: Aaltodoc (Aalto University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Precision benchmarks for solids: G0W0 calculations with different basis sets The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained from a Density Functional Theory (DFT) calculation are used as a starting point to build the Green's function G and the screened Coulomb interaction W, yielding the one-shot G0W0 selfenergy if no further update of these quantities are made. Multiple implementations exist for both the DFT and the subsequent G0W0 calculation, leading to possible differences in quasiparticle energies. In the present work, the G0W0 quasiparticle energies for states close to the band gap are calculated for six crystalline solids, using four different codes: Abinit, exciting, FHI-aims, and GPAW. This comparison helps to assess the impact of basis-set types (planewaves versus localized orbitals) and the treatment of core and valence electrons (all-electron full potentials versus pseudopotentials). The impact of unoccupied states as well as the algorithms for solving the quasiparticle equation are also briefly discussed. For the KS-DFT band gaps, we observe good agreement between all codes, with differences not exceeding 0.1 eV, while the G0W0 results deviate on the order of 0.1-0.3 eV. Between all-electron codes (FHI-aims and exciting), the agreement is better than 15 meV for KS-DFT and, with one exception, about 0.1 eV for G0W0 band gaps. Basis (linear algebra) ; Econometrics ; Computer science ; Mathematics ; Geometry Thermal Expansion and Ionic Conductivity ; Advanced ceramic materials synthesis
Authors: Tahmid Choudhury | Chen Huang | Aakash Varambhia | Alessandro Turrina | Mervyn D. Shannon | Doğan Özkaya | Angus I. Kirkland
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Chemistry — Inorganic Chemistry

Quantitative Electron Microscopy of Zeolites Using Aberration Corrected (S)TEM and Machine Learning Electron microscope ; Materials science ; Transmission electron microscopy ; Microscopy ; Optics ; Nanotechnology ; Physics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Wei Li
Source: Preprints.org (preprint, 2024-09-11)
Field: Chemistry — Organic Chemistry

Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and Ca&lt;sub&gt;V&lt;/sub&gt;2.2 The optimization of ligand-receptor binding affinities is essential for enhancing therapeutic efficacy and specificity, particularly in the design of peptide-based drugs. ziconotide, a potent blocker of the N-type calcium channel CaV2.2, has demonstrated therapeutic potential in the treatment of moderate to severe chronic pain. However, there is limited structural and binding affinity data available for scaling the design of ziconotide analogues with improved efficacy and specificity. In this study, I present a structural biophysics-based approach to develop a scalable in silico framework for generating and analyzing ligand-receptor binding affinity landscapes, focusing on ziconotide and its receptor, CaV2.2. This framework integrates advanced computational structural tools and binding affinity calculations to produce high-accuracy structural and intermolecular binding data. The insights gained from this scalable approach will guide the design of ziconotide analogues with enhanced efficacy, offering a powerful computational workflow for high-throughput ligand optimization. Additionally, when combined with artificial intelligence (AI) algorithms, this computational workflow generates high-accuracy structural and biophysical data, towards an AI- and structural biophysics-driven paradigm shift in peptide discovery and design. Scalability ; Ligand (biochemistry) ; Chemistry ; Receptor ; Computer science ; Biochemistry ; Operating system Chemical synthesis and alkaloids ; Cholinesterase and Neurodegenerative Diseases ; Nicotinic Acetylcholine Receptors Study
Authors: Andrea Griesi | R. Le Van Mao | Sirous Khabbazabkenar | Yurii P. Ivanov | Guohua Jia | Giorgio Divitini
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Multidimensional Characterisation and Multivariate Analysis of Cu3P Nanocages for Catalysis Nanocages ; Multivariate statistics ; Multivariate analysis ; Catalysis ; Chemistry ; Computer science ; Organic chemistry ; Machine learning Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Hongyi Guan | Negar Ahani | Carlos J. Garcı́a-Cervera | Ananya Renuka Balakrishna
Source: arXiv (Cornell University) (preprint, 2024-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetoelastic Interactions Reduce Hysteresis in Soft Magnets The width of the magnetic hysteresis loop is often correlated with the material's magnetocrystalline anisotropy constant $κ_1$. Traditionally, a common approach to reduce the hysteresis width has been to develop alloys with $κ_1$ as close to zero as possible. However, contrary to this widely accepted view, we present evidence that magnetoelastic interactions governed by magnetostriction constants, elastic stiffness, and applied stresses play an important role in reducing magnetic hysteresis width, despite large $κ_1$ values. We use a nonlinear micromagnetics framework to systematically investigate the interplay between material constants $λ_{100}$, $c_{11}$, $c_{12}$, $κ_1$, applied or residual stresses $σ_{\mathrm{R}}$, and needle domains to collectively lower the energy barrier for magnetization reversal. A distinguishing feature of our work is that we correlate the energy barrier governing the growth of needle domains with the width of the hysteresis loop. This energy barrier approach enables us to capture the nuanced interplay between anisotropy constant, magnetostriction, and applied stresses, and their combined influence on magnetic hysteresis. We propose a mathematical relationship on the coercivity map: $κ_1 = α(c_{11}-c_{12})(λ_{100}+βσ_{11})^2$ for which magnetic hysteresis can be minimized for a uniaxial residual stress $σ_\mathrm{R} = σ_{11}\hat{\mathbf{e}}_1\otimes\hat{\mathbf{e}}_1$ (and for some constants $α$, $β$). These results serve as quantitative guidelines to design magnetic alloys with small hysteresis, and potentially guide the discovery of a new generation of soft magnets located beyond the $κ_1 \to 0$ region. Hysteresis ; Magnet ; Materials science ; Condensed matter physics ; Mechanical engineering ; Physics ; Engineering Magnetic Properties and Applications ; Magnetic Properties of Alloys
Authors: Peichen Li | Mingxuan Wu | Weibing Wu
Source: Applied and Computational Engineering (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Neural network-based Raman spectral recognition model for milk powder and wheat flour The problem of counterfeit milk powder has occurred in recent years, and one of the standard techniques is to adulterate milk powder with wheat flour to reduce the cost. Since the morphology and colour of wheat flour are similar to that of milk powder, it is not easy to distinguish it from the naked eye, so a rapid method for detecting wheat flour adulteration in milk powder is urgently needed. This study used Raman spectral analysis as a novel and efficient screening technique. With the advantages of high sensitivity, non-destructive detection, low cost, no contamination, and online analysis, this method can play an essential role in rapid food detection and production control. By processing and analyzing the spectral data, a neural network model for identifying milk powder and wheat flour based on Raman spectroscopy has been developed in this study, and the model's performance has been evaluated. The results show that the model can quickly and accurately identify the proportion of wheat flour in milk powder to determine whether milk powder is adulterated or not. The results of this study are of great significance in ensuring the safety of infant milk powder. In the future, the method can be further optimized, for example, to improve detection speed and accuracy and to provide a more reliable quality control method for the milk powder industry. Wheat flour ; Food science ; Raman spectroscopy ; Materials science ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sergey M. Gertsik | Vladislav A. Koniukhov | Sergey V. Lekomtsev
Source: Вестник Пермского университета Математика Механика Информатика (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Numerical Modeling of Cylindrical Shell Fragmentation Knowledge of the fragmentation of a structure during its rapid destruction is essential for ensuring the safety of people and surrounding objects. The characteristics of fragments and their space-mass distribution obtained as a result of experiments are essentially inhomogeneous. This may be due to the inhomogeneity of the structure made of real materials, which should be taken into account in calculations. The paper presents the results of numerical modeling of the problem of cylindrical shell fragmentation taking into account the probability distribution of the fracture parameter. The calculations are performed in the LOGOS software using the finite element method. Fracture of the shell is realized by separating the nodal bonds. The criterion for the onset of fracture is the exceedance of the critical value of plastic strains. The validity of the obtained results is confirmed by their agreement with the experimental data and convergence of the solution with increasing number of nodal unknowns. The influence of the fracture criterion, which is set as a material constant and as a parameter distributed according to the normal probability law, is analyzed. It is shown that the use of the probabilistic fracture criterion allows predicting the parameters of the fragmentation field, which are qualitatively consistent with the experimental data. It is established that the degree of shell discretization has a significant effect on the fragment parameters, but practically does not change the type of their mass distribution. Fragmentation (computing) ; Shell (structure) ; Mechanics ; Numerical modeling ; Materials science ; Physics ; Computer science ; Composite material ; Geophysics ; Operating system High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Structural Response to Dynamic Loads
Authors: Hanlong Li | Pei Wang | Yuhan Wu | Jing Ren | Yuhang Gao | Lingyun Zhang | Mingtai Zhang | Wenxing Chen
Source: arXiv (Cornell University) (preprint, 2024-05-29)
Field: Chemistry — Organic Chemistry

WLC-Net: a robust and fast deep-learning wood-leaf classification method Wood-leaf classification is an essential and fundamental prerequisite in the analysis and estimation of forest attributes from terrestrial laser scanning (TLS) point clouds,including critical measurements such as diameter at breast height(DBH),above-ground biomass(AGB),wood volume.To address this,we introduce the Wood-Leaf Classification Network(WLC-Net),a deep learning model derived from PointNet++,designed to differentiate between wood and leaf points within tree point clouds.WLC-Net enhances classification accuracy,completeness,and speed by incorporating linearity as an inherent feature,refining the input-output framework,and optimizing the centroid sampling technique.WLC-Net was trained and assessed using three distinct tree species datasets,comprising a total of 102 individual tree point clouds:21 Chinese ash trees,21 willow trees,and 60 tropical trees.For comparative evaluation,five alternative methods,including PointNet++,DGCNN,Krishna Moorthy's method,LeWoS, and Sun's method,were also applied to these datasets.The classification accuracy of all six methods was quantified using three metrics:overall accuracy(OA),mean Intersection over Union(mIoU),and F1-score.Across all three datasets,WLC-Net demonstrated superior performance, achieving OA scores of 0.9778, 0.9712, and 0.9508;mIoU scores of 0.9761, 0.9693,and 0.9141;and F1-scores of 0.8628, 0.7938,and 0.9019,respectively.The time costs of WLC-Net were also recorded to evaluate the efficiency.The average processing time was 102.74s per million points for WLC-Net.In terms of visual inspect,accuracy evaluation and efficiency evaluation,the results suggest that WLC-Net presents a promising approach for wood-leaf classification,distinguished by its high accuracy. In addition,WLC-Net also exhibits strong applicability across various tree point clouds and holds promise for further optimization. Net (polyhedron) ; Artificial intelligence ; Deep learning ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Geometry Wood and Agarwood Research ; Remote Sensing and LiDAR Applications
Authors: Ruizhanghui Wang | Jiarui Zhang | Jianyu CHEN | Yuxin Miao | Jiwan Han | Lijun Cheng
Source: INMATEH Agricultural Engineering (article, 2024-12-21)
Field: Chemistry — Analytical Chemistry

RECOGNITION OF DROUGHT STRESS IN MILLET ON HYPERSPECTRAL IMAGING Millets are one of China's primary traditional food crops, and drought can adversely impact their yield and quality. To quickly detect the degree of drought stress in cereal grains, this study establishes a nondestructive classification model based on hyperspectral imaging technology. The raw spectral data underwent preprocessing using six pretreatment methods and various combinations of these methods. Subsequently, three distinct algorithms were employed for feature wavelength selection. To assess the severity of drought stress on millet, classification models were developed by integrating texture and color features, utilizing Support Vector Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA), and Multilayer Perceptron (MLP) algorithms. The results indicate that the D1st-SVM model, based on CARS wavelength selection, exhibits the highest modeling performance when feature wavelengths are fused with significant texture and color variables, achieving an accuracy rate of 93%. These findings suggest that drought identification in millet can be performed quickly and nondestructively by integrating image features through hyperspectral imaging technology. Hyperspectral imaging ; Drought stress ; Agronomy ; Environmental science ; Stress (linguistics) ; Water stress ; Remote sensing ; Agricultural engineering ; Biology ; Engineering ; Geology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Md. Toukir Ahmed | Villordon, Arthur | Kamruzzaman, Mohammed
Source: arXiv (Cornell University) (preprint, 2024-05-22)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications. Hyperspectral imaging ; Artificial intelligence ; Agriculture ; Computer science ; Computer vision ; Pattern recognition (psychology) ; Agricultural engineering ; Biology ; Engineering ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2024-08-25)
Field: Chemistry — Organic Chemistry

FURANOCOUMARIN DERIVED VIRTUAL SCREENING AND MOLECULAR DOCKING TO DISCOVER NOVEL, MTOR RECEPTOR MODULATORS FOR BREAST CANCER In order to develop successful treatments for breast cancer, it is crucial to understand the binding interactions between furanocoumarin and the human epidermal growth factor receptor, or mTOR.This study uses computational methods to achieve just that.This study offers a thorough computational approach to find putative mTOR inhibitors with an emphasis on treating breast cancer.The study begins with the selection of bioactive compounds from the ChEMBL 2D database, utilizing a combination of structural and pharmacophorebased methodologies.Molecular docking simulations were performed using the indicated medicines using the PDB-REDO refined crystal structure of the Kinase domain of mTOR inhibitor.After refining, the mTOR domain's structural quality was increased, as evidenced by the Kleywegt-like plot analysis, which also showed improved agreement with experimental results.Using the AutoDock program, molecular docking simulations are performed to determine the binding affinity and interaction patterns of certain drugs with the mTOR receptor.Compounds with strong binding affinities, advantageous interaction patterns, and structural compatibility are highlighted in virtual screening findings as possible lead candidates.ADMETox filtration was used to guarantee safety and effectiveness, offering information on the pharmacokinetic characteristics and toxicity profile of the molecule.Tamoxifen (C26H29NO), the chosen chemical, has a projected inactivity across a number of toxicity classes and endpoints and a generally good safety profile.Although immunotoxicity is anticipated, there appears to be little risk overall, based on the low probability.Pharmacokinetic and physicochemical analyses suggest that tamoxifen has the potential to be developed as a medication.The molecule satisfies drug-likeness criteria with its molecular weight of 371.5g/mol, balanced lipophilicity, and good gastrointestinal absorption. Virtual screening ; Furanocoumarin ; Docking (animal) ; Computational biology ; Breast cancer ; Discovery and development of mTOR inhibitors ; PI3K/AKT/mTOR pathway ; Computer science ; Cancer ; Cancer research ; Chemistry ; Biology ; Bioinformatics ; Medicine ; Genetics ; Drug discovery ; Biochemistry ; Signal transduction Synthesis and biological activity ; Click Chemistry and Applications ; Computational Drug Discovery Methods