jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heitor Luiz Ornaghi | Roberta Motta Neves | Francisco Maciel Monticeli
Source: Textiles (article, 2021-08-07)
Field: Materials Science — Polymers and Plastics

Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers Lignocellulosic fibers are widely applied as reinforcement in polymer composites due to their properties. The thermal degradation behavior governs the maximum temperature at which the fiber can be applied without significant mass loss. It is possible to determine this temperature using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined by using Arrhenius-based equations, and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost and time. Methods to predict thermogravimetric curves can be very useful in the materials science field, and in this sense, mathematical approaches are powerful tools, if well employed. For this reason, in the present study, thermogravimetric curves from curaua fiber were obtained at four different heating rates (5, 10, 20 and 40 °C·min−1) and Vyazovkin kinetic parameters were obtained using free available software. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM) aiming to obtain curves at any heating rate between the minimum and maximum experimental heating rates. Finally, Vyazovkin kinetic parameters were tested again, with the new predicted curves at the heating rates of 7, 15, 30 and 50 °C·min−1. Similar values of the kinetic parameters were obtained compared to the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications. Thermogravimetric analysis ; Kinetic energy ; Fiber ; Arrhenius equation ; Artificial neural network ; Biological system ; Curve fitting ; Materials science ; Thermal ; Polymer ; Thermodynamics ; Computer science ; Composite material ; Chemistry ; Activation energy ; Artificial intelligence ; Machine learning ; Physical chemistry ; Physics ; Organic chemistry Natural Fiber Reinforced Composites ; Thermal and Kinetic Analysis ; Polymer crystallization and properties
Authors: Wei Wang | Senjun Yao | Shengwei Deng | Yinbin Wang | Chenglong Qiu | Chengli Mao | Jianguo Wang
Source: Langmuir (article, 2021-10-24)
Field: Materials Science — Materials Chemistry

Sintering Rate and Mechanism of Supported Pt Nanoparticles by Multiscale Simulation Thermal stability is the key issue in the industrial application of supported metal nanocatalysts. A combination method of density functional theory calculations, machine learning, and molecular dynamics simulation is adopted to study the sintering behavior of supported platinum (Pt) nanoparticles on graphene or TiO<sub>2</sub> nanosheet, and analyze sintering mechanisms under different temperatures, particle sizes, and metal support interactions (MSIs). The results show that the agglomeration of supported nanoparticles is mainly based on the mechanism of small particle migration and growth. Small-sized particles with high surface energy determine the sintering rate. In addition, the increase of temperature is conducive to the agglomeration of particles, especially for systems with strong MSI. Based on the analysis of the sintering process, a sintering kinetic model of supported Pt nanoparticles related to particle size, temperature, and MSI is established, which provides theoretical guidance for the design of supported metal catalysts with high thermal stability. Sintering ; Economies of agglomeration ; Materials science ; Nanoparticle ; Nanomaterial-based catalyst ; Particle (ecology) ; Nanosheet ; Molecular dynamics ; Particle size ; Nanotechnology ; Chemical engineering ; Metallurgy ; Chemistry ; Computational chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; nanoparticles nucleation surface interactions
Authors: Rhys E. A. Goodall | Alpha A. Lee
Source: Soft Matter (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-driven approximations to the bridge function yield improved closures for the Ornstein–Zernike equation A key challenge for soft materials design and coarse-graining simulations is determining interaction potentials between components that give rise to desired condensed-phase structures. In theory, the Ornstein-Zernike equation provides an elegant framework for solving this inverse problem. Pioneering work in liquid state theory derived analytical closures for the framework. However, these analytical closures are approximations, valid only for specific classes of interaction potentials. In this work, we combine the physics of liquid state theory with machine learning to infer a closure directly from simulation data. The resulting closure is more accurate than commonly used closures across a broad range of interaction potentials. Zernike polynomials ; Ornstein–Zernike equation ; Bridge (graph theory) ; Yield (engineering) ; Statistical physics ; Function (biology) ; Physics ; Mathematics ; Mathematical analysis ; Integral equation ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Sunhwa Park | James P. Bailey | Hans J. Pasman | Qingsheng Wang | Mahmoud M. El‐Halwagi
Source: Computers & Chemical Engineering (article, 2021-09-05)
Field: Materials Science — Materials Chemistry

Fast, easy-to-use, machine learning-developed models of prediction of flash point, heat of combustion, and lower and upper flammability limits for inherently safer design Flash point ; Flammability ; Flammability limit ; Combustion ; Quantitative structure–activity relationship ; Computer science ; Algorithm ; Predictive modelling ; Limit (mathematics) ; Linear regression ; Machine learning ; Artificial neural network ; Mathematics ; Biological system ; Chemistry ; Thermodynamics ; Physics Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Risk and Safety Analysis
Authors: Ce Hu | Qidong Lin | Hua Guo | Bin Jiang
Source: Chemical Physics (article, 2021-11-26)
Field: Materials Science — Materials Chemistry

Influence of supercell size on Gas-Surface Scattering: A case study of CO scattering from Au(1 1 1) Supercell ; Dissipation ; Chemistry ; Molecular dynamics ; Scattering ; Molecule ; Chemical physics ; Surface (topology) ; Potential energy surface ; Molecular physics ; Density functional theory ; Computational chemistry ; Physics ; Thermodynamics ; Optics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Xingyu Ma | Hou-Yi Lyu | Kuan-Rong Hao | Zhen‐Gang Zhu | Qing‐Bo Yan | Gang Su
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

High-efficient <i>ab initio</i> Bayesian active learning method and applications in prediction of two-dimensional functional materials Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the <i>ab initio</i> Bayesian active learning method that combines active learning and high-throughput <i>ab initio</i> calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials. Ab initio ; Computer science ; Property (philosophy) ; Binary number ; Range (aeronautics) ; Artificial intelligence ; Bayesian probability ; Machine learning ; Density functional theory ; Materials science ; Algorithm ; Computational chemistry ; Mathematics ; Physics ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Ching‐Yu Wang | T. S. Ko | Cheng‐Che Hsu
Source: Plasma Processes and Polymers (article, 2021-09-13)
Field: Chemistry — Electrochemistry

Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution Abstract This study presents an explainable artificial intelligence (XAI) vision for optical emission spectroscopy (OES) of plasma in aqueous solution. We aim to characterize the plasma and OES with XAI. Trained with 18000 spectra, a multilayer artificial neural network (ANN) model accurately predicted the solution conductivity. Local interpretable model‐agnostics explanations (LIME), an XAI method, interpreted the model through perturbing spectral features and fitting the feature contribution with a linear model. LIME showed that OH, H γ , and H β emission lines were critical to the model, differing from the lines typically selected by humans. The results demonstrated that machine captured the spectral features neglected by humans. We believe using XAI for plasma OES analysis impacts the fields of plasma and analytical chemistry. Aqueous solution ; Plasma ; Spectroscopy ; Conductivity ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Characterization (materials science) ; Materials science ; Analytical Chemistry (journal) ; Biological system ; Chemistry ; Computer science ; Physics ; Nanotechnology ; Chromatography ; Physical chemistry Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Junjie Qiu | Fan Lü | Hua Zhang | Liming Shao | Pinjing He
Source: Trends in Environmental Analytical Chemistry (article, 2021-06-02)
Field: Chemistry — Analytical Chemistry

Data mining strategies of molecular information for inspecting wastewater treatment by using UHRMS Wastewater ; Biochemical engineering ; Sewage treatment ; Computer science ; Process engineering ; Data mining ; Environmental science ; Chemistry ; Engineering ; Environmental engineering Analytical chemistry methods development ; Water Quality Monitoring and Analysis ; Mass Spectrometry Techniques and Applications
Authors: Alexa B. Hanson | Ian J. Schwerdt | Cody A. Nizinski | Rachel Nicholls Lee | Nicholas J. Mecham | Erik C. Abbott | Sean Heffernan | Adam M. Olsen | Michael R. Klosterman | Sean P. Martinson | Alexandria Brenkmann | Luther W. McDonald
Source: ACS Omega (article, 2021-03-16)
Field: Chemistry — Inorganic Chemistry

Impact of Controlled Storage Conditions on the Hydrolysis and Surface Morphology of Amorphous-UO<sub>3</sub> The hydration and morphological effects of amorphous (A)-UO<sub>3</sub> following storage under varying temperature and relative humidity have been investigated. This study provides valuable insight into U-oxide speciation following aging, the U-oxide quantitative morphological data set, and, overall, the characterization of nuclear material provenance. A-UO<sub>3</sub> was synthesized via the washed uranyl peroxide synthetic route and aged based on a 3-factor circumscribed central composite design of experiment. Target aging times include 2.57, 7.00, 14.0, 21.0, and 25.4 days, temperatures of 5.51, 15.0, 30.0, 45.0, and 54.5 °C, and relative humidities of 14.2, 30.0, 55.0, 80.0, and 95.8% were examined. Following aging, crystallographic changes were quantified via powder X-ray diffraction and an internal standard Rietveld refinement method was used to confirm the hydration of A-UO<sub>3</sub> to crystalline schoepite phases. The particle morphology from scanning electron microscopy images was quantified using both the Morphological Analysis of MAterials software and machine learning. Results from the machine learning were processed via agglomerative hierarchical clustering analysis to distinguish trends in morphological attributes from the aging study. Significantly hydrated samples were found to have a much larger, plate-like morphology in comparison to the unaged controls. Predictive modeling via a response surface methodology determined that while aging time, temperature, and relative humidity all have a quantifiable effect on A-UO<sub>3</sub> crystallographic and morphological changes, relative humidity has the most significant impact. Relative humidity ; Materials science ; Amorphous solid ; Morphology (biology) ; Scanning electron microscope ; Uranyl ; Chemical engineering ; Crystallography ; Chemistry ; Composite material ; Metallurgy ; Geology Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Sabrya E. van Heijst | Masaki Mukai | Eiji Okunishi | Hiroki Hashiguchi | Laurien I. Roest | Louis Maduro | Juan Rojo | Sonia Conesa‐Boj
Source: Annalen der Physik (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Illuminating the Electronic Properties of WS<sub>2</sub> Polytypism with Electron Microscopy Abstract Tailoring the specific stacking sequence (polytypes) of layered materials represents a powerful strategy to identify and design novel physical properties. While nanostructures built upon transition‐metal dichalcogenides (TMDs) with either the 2H or 3R crystalline phases have been routinely studied, knowledge of TMD nanomaterials based on mixed 2H/3R polytypes is far more limited. In this work, mixed 2H/3R free‐standing WS 2 nanostructures displaying a flower‐like configuration are fingerprinted by means of state‐of‐the‐art transmission electron microscopy. Their rich variety of shape‐morphology configurations is correlated with relevant local electronic properties such as edge, surface, and bulk plasmons. Machine learning is deployed to establish that the 2H/3R polytype displays an indirect band gap of . Further, high resolution electron energy‐loss spectroscopy reveals energy‐gain peaks exhibiting a gain‐to‐loss ratio greater than unity, a property that can be exploited for cooling strategies of atomically‐thin TMD nanostructures and devices built upon them. The findings of this work represent a stepping stone towards an improved understanding of TMD nanomaterials based on mixed crystalline phases. Materials science ; Stacking ; Nanomaterials ; Electron energy loss spectroscopy ; Nanostructure ; Transmission electron microscopy ; Plasmon ; Nanotechnology ; Electron ; Scanning transmission electron microscopy ; Optoelectronics ; Chemical physics ; Physics ; Nuclear magnetic resonance 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Seung Jae Kwak | Hyo Seok Kim | Namjun Park | Myung‐June Park | Won Bo Lee
Source: Korean Journal of Chemical Engineering (article, 2021-05-31)
Field: Chemistry — Inorganic Chemistry

Recent progress on Al distribution over zeolite frameworks: Linking theories and experiments Zeolite ; Aluminium ; Adsorption ; Catalysis ; Biochemical engineering ; Cationic polymerization ; Work (physics) ; Distribution (mathematics) ; Biological system ; Computer science ; Chemistry ; Nanotechnology ; Computational chemistry ; Materials science ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Mathematics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Bhaskar Sarkar | Santhosh R. Gundlapally | Panagiotis Koutsivitis | Syed Wahiduzzaman
Source: Chemical Engineering Journal (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Performance evaluation of neural networks in modeling exhaust gas aftertreatment reactors Nonlinear autoregressive exogenous model ; Akaike information criterion ; Artificial neural network ; Autoregressive model ; Diesel fuel ; Automotive engineering ; Computer science ; Engineering ; Machine learning ; Mathematics Catalytic Processes in Materials Science ; Fault Detection and Control Systems ; Neural Networks and Applications
Authors: Victor García Satorras | Emiel Hoogeboom | Fabian B. Fuchs | Ingmar Posner | Max Welling
Source: arXiv (Cornell University) (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

E(n) Equivariant Normalizing Flows for Molecule Generation in 3D This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph neural networks and integrate them as a differential equation to obtain an invertible equivariant function: a continuous-time normalizing flow. We demonstrate that E-NFs considerably outperform baselines and existing methods from the literature on particle systems such as DW4 and LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our knowledge, this is the first likelihood-based deep generative model that generates molecules in 3D. Equivariant map ; Generative grammar ; Invertible matrix ; Discriminative model ; Euclidean geometry ; Mathematics ; Homogeneous space ; Graph ; Computer science ; Pure mathematics ; Combinatorics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Elliot K. Anyidoho | Ernest Teye | Robert Agbemafle | Charles Lloyd Yeboah Amuah | Vida Gyimah Boadu
Source: Journal of Food Processing and Preservation (article, 2021-03-16)
Field: Chemistry — Analytical Chemistry

Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters Fermentation duration (FmD), fermentation index (FI), pH, and moisture content (Mc) are vital quality attributes of cocoa beans. In this study, portable near infrared spectroscopy (NIRS) and multivariate analyses were used for rapid determination of FmD, FI, pH, and Mc of cocoa beans. The samples were scanned in 900- to 1,700-nm wavelength, and the spectral data were pretreated independently with first derivatives (FD) and second derivatives (SD), multiplicative scatter correction (MSC), mean centering (MC), and standard normal variate (SNV), while linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares regression (PLS-R) were used to build the prediction models for FmD, FI, pH, and Mc. MSC plus SVM gave an accurate classification of 100%. For predicting FI, pH, and Mc, the PLS-R model gave coefficient of correlation of 0.87, 0.82, and 0.89, respectively. The results showed that portable NIRS could be employed for cocoa bean examination. Novelty impact statement Fermentation is the single most essential postharvest operation that influences cocoa beans quality parameters including moisture content, fermentation index (FI) and pH. Unlike stationary laboratory based wet chemistry technique or table top NIR spectroscopy, this study revealed that the relatively inexpensive portable NIR spectroscopy could provide very fast (within 30 s) results in the routine onsite evaluation of cocoa beans moisture content, fermentation index and pH on farmers field in Sub-Saharan Africa. In particular, the study outcome highlights the potential application of portable NIR spectroscopy based on machine learning for efficient classification of fermentation duration and quantification of moisture content & pH of cocoa beans in real-time usage. Partial least squares regression ; Fermentation ; Near-infrared spectroscopy ; Chemometrics ; Linear discriminant analysis ; Chemistry ; Mathematics ; Support vector machine ; COCOA BEAN ; Water content ; Food science ; Artificial intelligence ; Statistics ; Chromatography ; Computer science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Food Chemistry and Fat Analysis ; Meat and Animal Product Quality
Authors: Olivier Charles Gagné
Source: Chemical Science (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

On the crystal chemistry of inorganic nitrides: crystal-chemical parameters, bonding behavior, and opportunities in the exploration of their compositional space The scarcity of nitrogen in Earth's crust, combined with challenging synthesis, have made inorganic nitrides a relatively unexplored class of compounds compared to their naturally abundant oxide counterparts. To facilitate exploration of their compositional space <i>via a priori</i> modeling, and to help <i>a posteriori</i> structure verification not limited to inferring the oxidation state of redox-active cations, we derive a suite of bond-valence parameters and Lewis acid strength values for 76 cations observed bonding to N<sup>3-</sup>, and further outline a baseline statistical knowledge of bond lengths for these compounds. Examination of structural and electronic effects responsible for the functional properties and anomalous bonding behavior of inorganic nitrides shows that many mechanisms of bond-length variation ubiquitous to oxide and oxysalt compounds (<i>e.g.</i>, lone-pair stereoactivity, the Jahn-Teller and pseudo Jahn-Teller effects) are similarly pervasive in inorganic nitrides, and are occasionally observed to result in greater distortion magnitude than their oxide counterparts. We identify promising functional units for exploring uncharted chemical spaces of inorganic nitrides, <i>e.g.</i> multiple-bond metal centers with promise regarding the development of a post-Haber-Bosch process proceeding at milder reaction conditions, and promote an atomistic understanding of chemical bonding in nitrides relevant to such pursuits as the development of a model of ion substitution in solids, a problem of great relevance to semiconductor doping whose solution would fast-track the development of compound solar cells, battery materials, electronics, and more. Nitride ; Crystal chemistry ; Chemical bond ; Chemical space ; Crystal (programming language) ; Crystal structure ; Chemistry ; Identification (biology) ; Nanotechnology ; Materials science ; Chemical physics ; Crystallography ; Computer science ; Organic chemistry Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science