jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenjiang Zhou | Bai Song
Source: Physical review. B./Physical review. B (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Isotope effect on four-phonon interaction and lattice thermal transport: An atomistic study of lithium hydride Despite decades-long studies of how isotope-disorder scattering affects heat conduction, the effect of isotope-induced frequency shift on phonon-phonon interactions and lattice thermal transport remains largely unexplored and less understood. In this work, by using the phonon Boltzmann transport equation and machine-learning-assisted classical and path-integral molecular-dynamics simulations, we theoretically investigate the interplay between the intrinsic phonon-phonon scatterings and the isotopic compositions in lithium hydride (LiH). We reveal that the hydrogen isotopes notably impact thermal transport primarily via three-phonon interaction, while four-phonon scattering is less sensitive. Due to the stronger temperature dependence of higher-order processes, the isotope effect on thermal conductivity is increasingly suppressed by four-phonon scattering as temperature rises (from 73% to 35% at 700 K). In addition, we observe a more pronounced isotope effect on thermal diffusivity, which is attributed to the variation of heat capacity. Our work provides a promising avenue for manipulating thermal transport via isotope engineering, and it highlights the crucial role of four-phonon interaction. Phonon ; Lattice (music) ; Kinetic isotope effect ; Lithium (medication) ; Hydride ; Lithium hydride ; Isotope ; Condensed matter physics ; Materials science ; Ion ; Thermal ; Chemical physics ; Physics ; Atomic physics ; Deuterium ; Nuclear physics ; Hydrogen ; Thermodynamics ; Quantum mechanics ; Ionic bonding Thermal properties of materials ; Thermodynamic and Structural Properties of Metals and Alloys ; Nuclear Materials and Properties
Authors: Justin A. Kaskow | Eric T. Hahnert | Thomas Porter | Yali Lu | Valentin Stanev | Chendi Niu | Wei Xu | Methal Albarghouthi | Chunlei Wang
Source: Journal of the American Society for Mass Spectrometry (article, 2024-09-09)
Field: Chemistry — Spectroscopy

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis. Chemistry ; Electrospray ionization ; Mass spectrometry ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Extractive electrospray ionization ; Desorption electrospray ionization ; Chromatography ; Ionization ; Protein mass spectrometry ; Ambient ionization ; Analytical Chemistry (journal) ; Peptide ; Capillary electrophoresis–mass spectrometry ; Chemical ionization ; Ion ; Organic chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Jiangtao Lv | Ruizhe Zhang | Chunli Liu | Zimeng Ge | Qiongchan Gu | Fu Feng | Guangyuan Si
Source: Optics & Laser Technology (article, 2024-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polarization-controlled metasurface design based on deep ResNet Residual neural network ; Polarization (electrochemistry) ; Materials science ; Computer science ; Optoelectronics ; Deep learning ; Artificial intelligence ; Chemistry Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: William Noh | Huck Beng Chew
Source: Extreme Mechanics Letters (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Dislocation descriptors of low and high angle grain boundaries with convolutional neural networks Multiscale modeling of plasticity in polycrystalline metals is a long-standing challenge in part because of the lack of an accepted grain boundary descriptor, which has hindered the bridging of scales between atomistic simulations and meso-scale discrete dislocation dynamics (DDD) models. While grain boundary dislocations (GBDs) for low angle grain boundaries can be ascertained by Burgers circuit analyses, the dislocation structures of high angle grain boundaries have remained elusive because of overlapping dislocation core fields. Here, we use convolutional neural networks (CNNs) to establish the locations of GBDs responsible for the misorientations of <001> symmetrical-tilt Cu grain boundaries, from the local atomistic stress fields modeled with molecular dynamics (MD) simulations. We achieve accurate CNN predictions of GBDs with sub-angstrom resolution across an extensive set of low- to high-tilt-angle grain boundaries through a training dataset, generated from superposing continuum-representations of the MD stress fields of single dislocations that consider the contributions of both the Volterra and dislocation core fields. The approach paves the way for dislocation representations of the atomistic grain boundary structures modeled by MD simulations or density functional theory (DFT) calculations in mesoscale DDD models to elucidate multiscale plasticity effects. Grain boundary ; Dislocation ; Materials science ; Molecular dynamics ; Grain boundary strengthening ; Plasticity ; Tilt (camera) ; Statistical physics ; Condensed matter physics ; Crystallite ; Convolutional neural network ; Geometry ; Crystallography ; Physics ; Mathematics ; Computer science ; Microstructure ; Artificial intelligence ; Chemistry ; Composite material ; Metallurgy Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Hemayet Hossain Tuhin | Md Abdul Kayum | Md Rahmatul Kabir Rasel Sarker | Shah Md Tanvir Siddiquee | Bishwajeet Pandey
Source:  (article, 2024-05-15)
Field: Chemistry — Inorganic Chemistry

Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the ’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry. Transfer of learning ; Computer science ; Artificial intelligence ; Deep learning ; Machine learning Coconut Research and Applications ; Smart Agriculture and AI ; Spectroscopy and Chemometric Analyses
Authors: Nashat Nawafleh | Faris M. AL‐Oqla
Source: Functional Composites and Structures (article, 2024-01-04)
Field: Materials Science — Polymers and Plastics

An effective hybrid particle swarm—artificial neural network optimization for predicting green bio-fiber mechanical characteristics and optimizing biomaterial performance Abstract Natural fiber-reinforced composites are currently utilized in several applications due to worldwide environmental and cost concerns. However, these composites have production challenges such as poor reinforcement-matrix adhesion, that sophisticates the prediction of their mechanical properties. This study presents a novel, robust hybrid particle swarm—artificial neural network optimization (PSO-ANN) methodology to assess and create accurate predictions of the green bio-fibers to optimize and improve the mechanical features of biomaterials for green bio-products instead of performing tedious experimental works. As the mechanical qualities of green bio-fibers might differ from one fiber to another due to several interacted parameters, high complexity in predicting the bio-fiber capabilities exists. Therefore, this work utilizes suitable methods with a non-linear activation function to predict the mechanical characteristics of natural fibers that allow the researchers to improve the choices of natural fibers for biomaterials on the basis of cellulose content, the microfibrillar angle, and the diameter of natural fibers, decreasing the duration of the process required to characterize materials experimentally. The reliability of the introduced PSO-ANN model was verified by the investigations of the fiber’s tensile stress and Young’s modulus. Results showed that the presented model is capable of consistently and accurately monitoring the mechanical performance to a large degree, in comparison with experimental results. This in fact would facilitate and simplify the process of selecting the best natural fiber composites, which speeds up the experimental characterization phase and improves energy efficiency in the process of converting energy into monetary income, which would have ramifications for both economies and ecosystems. The anticipated method would also boost scientific evaluation of green fibers, confirming their role as a replacement material for green product fulfillment in future eco-friendly manufacturing. Natural fiber ; Particle swarm optimization ; Fiber ; Materials science ; Artificial neural network ; Composite material ; Process (computing) ; Characterization (materials science) ; Computer science ; Process engineering ; Biological system ; Artificial intelligence ; Machine learning ; Engineering ; Nanotechnology Natural Fiber Reinforced Composites
Authors: Yujing Wen | Xingyue Liu | Feng He | Yanli Shi | Fanghui Chen | Wenfei Li | Youhong Song | Lin Li | Haiyang Jiang | Liang Zhou | Leiming Wu
Source: International Journal of Biological Macromolecules (article, 2024-09-28)
Field: Chemistry — Analytical Chemistry

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm Germplasm ; Lignin ; Stalk ; Fourier transform infrared spectroscopy ; Fourier transform ; Scale (ratio) ; Infrared ; Infrared spectroscopy ; Chemistry ; Spectroscopy ; Agronomy ; Materials science ; Horticulture ; Mathematics ; Biology ; Chemical engineering ; Physics ; Engineering ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Ratimanasee Sahu | Mohit Sharma | Peter Schall | Sarika Maitra Bhattacharyya | Vijayakumar Chikkadi
Source: Proceedings of the National Academy of Sciences (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Structural origin of relaxation in dense colloidal suspensions Amorphous solids relax via slow molecular rearrangement induced by thermal fluctuations or applied stress. Microscopic structural signatures predicting these structural relaxations have been long searched for but have so far only been found in dynamic quantities such as vibrational quasi-localized soft modes or with structurally trained neural networks. A physically meaningful structural quantity remains elusive. Here, we introduce a structural order parameter derived from the mean-field caging potential experienced by the particles due to their neighbors, which reliably predicts the occurrence of structural relaxations. The structural parameter, derived from density functional theory, provides a measure of susceptibility to particle rearrangements that can effectively identify weak or defect-like regions in disordered systems. Using experiments on dense colloidal suspensions, we demonstrate a strong correlation between this order parameter and the structural relaxations of the amorphous solid. In quiescent suspensions, this correlation increases with density, when particle rearrangements become rarer and more localized. In sheared suspensions, the order parameter reliably pinpoints shear transformations; the applied shear weakens the caging potential due to shear-induced structural distortions, causing the proliferation of plastic deformation at structurally weak regions. Our work paves the way to a structural understanding of the relaxation of a wide range of amorphous solids, from suspensions to metallic glasses. Amorphous solid ; Chemical physics ; Relaxation (psychology) ; Colloid ; Materials science ; Shear (geology) ; Particle (ecology) ; Thermal fluctuations ; Thermodynamics ; Chemistry ; Physics ; Crystallography ; Physical chemistry ; Composite material Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Semra Çi̇çek | Mustafa Tahsin Yılmaz | Tamara Dapčević Hadnađev | Eskindir Endalew Tadesse | Piotr Kulawik | Fatih Özoğul
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2024-06-21)
Field: Materials Science — Materials Chemistry

Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives Abstract Commercial applications of nanotechnology in the food industry are rapidly increasing. Accordingly, there is a simultaneous increase in the amount and diversity of nanowaste, which arise as byproducts in the production, use, disposal, or recycling processes of nanomaterials utilized in the food industry. The potential risks of this nanowaste to human health and the environment are alarming. It is of crucial significance to establish analytical methods and monitoring systems for nanowaste to ensure food safety. This review provides comprehensive information on nanowaste in foods as well as comparative material on existing and new analytical methods for the detection of nanowaste. The article is specifically focused on nanowaste in food systems. Moreover, the current techniques, challenges as well as potential use of new and progressive methods are underlined, further highlighting advances in technology, collaborative efforts, as well as future perspectives for effective nanowaste detection and tracking. Such detection and tracking of nanowaste are required in order to effectively manage this type ofwasted in foods. Although there are devices that utilize spectroscopy, spectrometry, microscopy/imaging, chromatography, separation/fractionation, light scattering, diffraction, optical, adsorption, diffusion, and centrifugation methods for this purpose, there are challenges to be overcome in relation to nanowaste as well as food matrix and method characteristics. New technologies such as radio‐frequency identification, Internet of things, blockchain, data analytics, and machine learning are promising. However, the cooperation of international organizations, food sector, research, and political organizations is needed for effectively managing nanowaste. Future research efforts should be focused on addressing knowledge gaps and potential strategies for optimizing nanowaste detection and tracking processes. Analytics ; Food sector ; Food industry ; Food safety ; Computer science ; Data science ; Risk analysis (engineering) ; Nanotechnology ; Biochemical engineering ; Business ; Engineering ; Materials science ; Chemistry ; Agriculture Nanoparticles: synthesis and applications ; Biosensors and Analytical Detection ; Electrochemical Analysis and Applications
Authors: Tahereh Shah Mansouri | Hui Wang | Davide Mariotti | Paul Maguire
Source: Journal of Physics D Applied Physics (article, 2024-05-23)
Field: Chemistry — Spectroscopy

Distinguishing methane from other hydrocarbons using machine learning and atmospheric pressure plasma optical emission spectroscopy Abstract The ability to detect gas molecule and assign a concentration offers an inventive solution in the field of plasma integrated with machine learning. The most important finding of this work is firstly, to develop an algorithm for gas-molecule identification using three different hydrocarbons (CH 4 , C 2 H 2 , C 2 H 6 ) and secondly, organize a model for detecting gas concentration (classification). For this reason, initially eight different gases evaluated. The study confirms the present of the unique emission lines as a gas indicator, i.e., a wavelength peak related to hydrocarbons identified via increasing in C x H y concentration. By means of unique variable important in projection, hydrocarbons can be distinguished. Our proposed Chemometric analysis strategy examined on &gt;1000 samples and results development of suitable techniques that are sufficiently rapid, accurate and innovative. This demonstrates the potential for real-time, portable, and continuous monitoring of trace gases with potential applications in medical, environmental, and industrial gas sensing. Methane ; Spectroscopy ; Atmospheric pressure ; Atmospheric-pressure plasma ; Plasma ; Atmospheric methane ; Materials science ; Environmental science ; Analytical Chemistry (journal) ; Chemistry ; Environmental chemistry ; Physics ; Meteorology ; Astronomy ; Organic chemistry ; Nuclear physics Mass Spectrometry Techniques and Applications ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yanying Zhang | Xinyan Zhu | Yuanzhong Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-03-08)
Field: Chemistry — Analytical Chemistry

Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves Eucommia ulmoides ; Partial least squares regression ; Artificial intelligence ; Computer science ; Support vector machine ; Aucubin ; Regression analysis ; Linear discriminant analysis ; Machine learning ; Predictive modelling ; Mathematics ; Pattern recognition (psychology) ; Data mining ; Chemistry ; Iridoid Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Yao Ji | Shuangli Dong | Weichao Wang | Qinyuan Zhang
Source: Journal of the American Ceramic Society (article, 2024-10-07)
Field: Materials Science — Ceramics and Composites

Advancing understanding of composition–structure–luminescent properties in laser glass through machine learnings Abstract Rare‐earth (RE)‐doped laser glasses meet urgent needs in national security and scientific fields, and their optimization has garnered extensive attention. However, the design of these laser glasses often relies excessively on trial‐and‐error experimentation, leading to significant costs and a lack of scientific guidance. Herein, we propose an integrated method that combines structural descriptors determined from molecular dynamics simulations, a self‐constructed luminescent database, and a machine learning algorithm to establish the composition–structure–luminescent property (CSLP) relationship. Using an Nd 3+ ‐doped commercial silicate laser glass system as an example, the effectiveness of this approach has been demonstrated. The developed CSLP model enables highly accurate predictions of spectral properties, achieving a determination coefficient ( R 2 ) greater than 0.94, based on eight structural descriptors. The importance of different structural descriptors on spectral characteristics is ranked and thoroughly discussed, revealing an intrinsic relationship between the first and second coordination shells around RE ions and luminescent behaviors. Furthermore, the generic structural descriptors identified in the CSLP model can be extrapolated to other systems involving different network formers (e.g., silicate and phosphate) and modifier cations (e.g., Li, Na, K, Ba, and Ca). This capability facilitates the design of laser glasses tailored to specific targets, such as large emission cross‐sections, extended lifetimes, or reduced quenching effects. Luminescence ; Laser ; Composition (language) ; Materials science ; Mineralogy ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Chemistry ; Optics ; Physics ; Engineering ; Art Glass properties and applications ; Surface Roughness and Optical Measurements ; Phase-change materials and chalcogenides
Authors: Utsav Prajapati | Rishit S. Shukla | Vidit B. Zala | Sanjeev K. Gupta | P. N. Gajjar
Source: International Journal of Hydrogen Energy (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Enhanced HER activity in Pd/Pt-decorated Janus XSeI (X = Sb, Bi) monolayers Janus ; Monolayer ; Materials science ; Palladium ; Chemistry ; Nanotechnology ; Crystallography ; Catalysis ; Organic chemistry 2D Materials and Applications ; Electrocatalysts for Energy Conversion ; Perovskite Materials and Applications