jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xingwang Li | Xiaofei Fan | Lili Zhao | Sheng Huang | Yi He | Xuesong Suo
Source: Applied Engineering in Agriculture (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning Highlights This study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN). Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM. In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement. Abstract . When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification. Convolutional neural network ; Artificial intelligence ; Support vector machine ; Multispectral image ; Pattern recognition (psychology) ; Pepper ; Feature (linguistics) ; Mathematics ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mahboobeh Babaei | Yavar T. Azar | Ali Sadeghi
Source: Physical review. B./Physical review. B (article, 2020-03-18)
Field: Materials Science — Materials Chemistry

Locality meets machine learning: Excited and ground-state energy surfaces of large systems at the cost of small ones It is demonstrated that supervised machine learning of local environments of the atoms in a molecule can be very effectively combined with the density functional-based tight-binding method to provide a fast and size-extensive scheme for electronic structure calculations. We train our machine learning model on small and basic molecules and then run it successfully for large and complicated molecules. This facilitates investigations of structural, electronic, and optical properties of large model systems for which the conventional iterative self-consistent procedure becomes too costly. The fruitfulness of this shortsightedness-based scheme is shown for describing the energy landscape of the ground and low-lying excited states of several model molecules of variant sizes and complexity. The achieved accuracy in the tests supports the locality view to the electronic redistribution in a molecule and promises the efficiency of the machine learning equipped divide-and-conquer approach for solving the Schr\"odinger equation. Locality ; Excited state ; Computer science ; Divide and conquer algorithms ; Ground state ; Scheme (mathematics) ; Electronic structure ; Energy (signal processing) ; Statistical physics ; Artificial intelligence ; Physics ; Algorithm ; Atomic physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Sheng Sun | Bochao Zhang | Jiahao Wang | Kaikai Li | Yao Gao | Tong‐Yi Zhang
Source: Journal of Chemometrics (article, 2020-11-02)
Field: Chemistry — Electrochemistry

Analytic formulas of peak current in cyclic voltammogram: Machine learning as an alternative way? Abstract Cyclic voltammetry is most widely used in the study of electrode kinetics. The peak current density ( I p ) in a cyclic voltammogram (CV) is a function of many parameters involved in the kinetics, thereby being an indicator of the reaction mechanism. Analytic expressions of I p for reversible and irreversible reactions, proposed by Randles–Sevcik equations via dimensional analysis and numerical solutions of dimensionless equations, play the central role in the analysis of experimental results of CV. Great difficulties are encountered, however, to derive an expression of I p in the quasireversible region by classical methods, and hence, an analytic formula is lacking yet to cover the entire reaction spectrum. The present work demonstrates the success of machine learning (ML) as an alternative way to find simple and analytic formulas of I p for the reversible and irreversible reactions and then for the entire reaction spectrum, purely based on data from CV simulations. Two analytic formulas of I p , both with high accuracy, are proposed by using symbolic regression combining with sparse regression. The simpler and physically meaningful one is obtained by combining ML with expert knowledge of mathematical expressions. The paper illustrates the powerful capability of ML, or by combining with expert knowledge, as a promising universal and practical tool to analyze complex electrochemical kinetics. Dimensionless quantity ; Cyclic voltammetry ; Regression ; Current (fluid) ; Applied mathematics ; Function (biology) ; Simple (philosophy) ; Work (physics) ; Computer science ; Mathematics ; Thermodynamics ; Chemistry ; Electrochemistry ; Electrode ; Statistics ; Physical chemistry ; Physics Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Molecular Junctions and Nanostructures
Authors: Estefanía Garijo del Río | Sami Kaappa | José A. Garrido Torres | Thomas Bligaard | Karsten Wedel Jacobsen
Source: The Journal of Chemical Physics (article, 2020-12-21)
Field: Materials Science — Materials Chemistry

Machine learning with bond information for local structure optimizations in surface science Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations. Kriging ; Kernel (algebra) ; Process (computing) ; Gaussian process ; Regression ; Computational learning theory ; Surface (topology) ; Energy (signal processing) ; Computational complexity theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Hossein Shokri | Erkinjon G. Nazarov | Ben Gardner | Hsein-Chi Niu | Gyoungil Lee | John A. Stone | Natividad Jurado-Campos | Gary A. Eiceman
Source: Analytical Chemistry (article, 2020-03-26)
Field: Chemistry — Spectroscopy

Field Induced Fragmentation (Fif) Spectra of Oxygen Containing Volatile Organic Compounds with Reactive Stage Tandem Ion Mobility Spectrometry and Functional Group Classification by Neural Network Analysis Mobility isolated spectra were obtained for protonated monomers of 42 volatile oxygen containing organic compounds at ambient pressure using a tandem ion mobility spectrometer with a reactive stage between drift regions. Fragment ions of protonated monomers of alcohols, acetates, aldehydes, ketones, and ethers were produced in the reactive stage using a 3.3 MHz symmetrical sinusoidal waveform with an amplitude of 1.4 kV and mobility analyzed in a 19 mm long drift region. The resultant field induced fragmentation (FIF) spectra included residual intensities for protonated monomers and fragment ions with characteristic drift times and peak intensities, associated with ion mass and chemical class. High efficiency of fragmentation was observed with single bond cleavage of alcohols and in six-member ring rearrangements of acetates. Fragmentation was not observed, or seen weakly, with aldehydes, ethers, and ketones due to their strained four-member ring transition states. Neural networks were trained to categorize spectra by chemical class and tested with FIF spectra of both familiar and unfamiliar compounds. Rates of categorization were class dependent with best performance for alcohols and acetates, moderate performance for ketones, and worst performance for ethers and aldehydes. Trends in the rates of categorization within a chemical family can be understood as steric influences on the energy of activation for ion fragmentation. Electric fields greater than 129 Td or new designs of reactive stages with improved efficiency of fragmentation will be needed to extend the practice of reactive stage tandem IMS to an expanded selection of volatile organic compounds. Chemistry ; Fragmentation (computing) ; Ion ; Protonation ; Analytical Chemistry (journal) ; Tandem mass spectrometry ; Mass spectrometry ; Organic chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Phyu Hnin Thike | Zhaoyang Zhao | Peng Liu | Feihu Bao | Ying Jin | Peng Shi
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning is performed to develop multiple hyperparameter combinations (network topologies) to train multiple ANNs with mini-batch stochastic gradient descent optimization algorithm to facilitate the training of a large dataset. After that, one selection strategy for the optimal hyperparameter combination is applied by an early stopping method to guarantee the generalization ability of the optimal network model. The correlation coefficients (R) of the ANN model can explain about 80% (more than 75%) of the variance of atmospheric corrosion of carbon steel, and the root mean square errors (RMSE) of three models show that the ANN model gives a better performance than the other two models with acceptable generalization. The influence of input parameters on the output is highlighted by using the fuzzy curve analysis method. The result reveals that TOW, Cl- and SO2 are the most important atmospheric chemical variables, which have a well-known nonlinear relationship with atmospheric corrosion. Hyperparameter ; Hyperparameter optimization ; Artificial neural network ; Early stopping ; Generalization ; Backpropagation ; Computer science ; Artificial intelligence ; Mean squared error ; Machine learning ; Mathematics ; Statistics ; Support vector machine Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Infrastructure Maintenance and Monitoring
Authors: Yasuhiro Ikabata | Ryo Fujisawa | Junji Seino | Takeshi Yoshikawa | Hiromi Nakai
Source: The Journal of Chemical Physics (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Machine-learned electron correlation model based on frozen core approximation The machine-learned electron correlation (ML-EC) model is a regression model in the form of a density functional that reproduces the correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC model was constructed using the correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC model. The valence-electron correlation energies and reaction energies calculated using the constructed model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange-correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile model. Extrapolation ; Electronic correlation ; Correlation ; Density functional theory ; Wave function ; Basis set ; Electron density ; Statistical physics ; Atomic physics ; Electron ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Elias Nehme | Daniel Z. Freedman | Racheli Gordon | Boris Ferdman | Lucien E. Weiss | Onit Alalouf | Tal Naor | Reut Orange | Tomer Michaeli | Yoav Shechtman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy. Microscopy ; Materials science ; Deep learning ; Artificial intelligence ; Nanotechnology ; Computer science ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Jie Liu | Kewang Yi | Zhaopeng Wang | Zijie Zhang | Yucong Qi | Pan Chen | Qun‐Dong Shen | Baojin Chu
Source: Science China Materials (article, 2020-11-12)
Field: Materials Science — Polymers and Plastics

All-organic composites with strong photoelectric response over a wide spectral range Materials science ; Poling ; Photoelectric effect ; Visible spectrum ; Composite material ; Infrared ; Ferroelectricity ; Photodetector ; Optoelectronics ; Polymer ; Photothermal therapy ; Dielectric ; Optics ; Nanotechnology Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Perovskite Materials and Applications
Authors: Mohamad Adil Iman Ishak | Mohd Faisal Taha | Mohd Dzul Hakim Wirzal | Muhammad Najib Nordin | Muslim Abdurrahman | Khairulazhar Jumbri
Source: Processes (article, 2020-04-01)
Field: Chemistry — Inorganic Chemistry

Choline-Based Ionic Liquids-Incorporated IRMOF-1 for H2S/CH4 Capture: Insight from Molecular Dynamics Simulation The removal of H2S and CH4 from natural gas is crucial as H2S causes environmental contamination, corrodes the gas stream pipelines, and decreases the feedstock for industrial productions. Many scientific researches have shown that the metal-organic framework (MOF)/ionic liquids (ILs) have great potential as alternative adsorbents to capture H2S. In this work, molecular dynamics (MD) simulation was carried out to determine the stability of ILs/IRMOF-1 as well as to study the solubility of H2S and CH4 gases in this ILs/IRMOF-1 hybrid material. Three choline-based ILs were incorporated into IRMOF-1 with different ratios of 0.4, 0.8, and 1.2% w/w, respectively, in which the most stable choline-based ILs/IRMOF-1 composite was analysed for H2S/CH4 solubility selectivity. Among the three choline-based ILs/IRMOF-1, [Chl] [SCN]/IRMOF-1 shows the most stable incorporation. However, the increment of ILs loaded in the IRMOF-1 significantly reduced the stability of the hybrid due to the crowding effect. Solvation free energy was then computed to determine the solubility of H2S and CH4 in the [Chl] [SCN]/IRMOF-1. H2S showed higher solubility compared to CH4, where its solubility declined with the increase of choline-based IL loading. Solubility ; Ionic liquid ; Molecular dynamics ; Chemical engineering ; Solvation ; Chemistry ; Materials science ; Inorganic chemistry ; Molecule ; Physical chemistry ; Organic chemistry ; Computational chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Industrial Gas Emission Control
Authors: Marcin Miklitz | Lukas Turcani | Rebecca L. Greenaway | Kim E. Jelfs
Source: Communications Chemistry (article, 2020-01-22)
Field: Chemistry — Organic Chemistry

Computational discovery of molecular C60 encapsulants with an evolutionary algorithm Computation is playing an increasing role in the discovery of materials, including supramolecular materials such as encapsulants. In this work, a function-led computational discovery using an evolutionary algorithm is used to find potential fullerene (C<sub>60</sub>) encapsulants within the chemical space of porous organic cages. We find that the promising host cages for C<sub>60</sub> evolve over the simulations towards systems that share features such as the correct cavity size to host C<sub>60</sub>, planar tri-topic aldehyde building blocks with a small number of rotational bonds, di-topic amine linkers with functionality on adjacent carbon atoms, high structural symmetry, and strong complex binding affinity towards C<sub>60</sub>. The proposed cages are chemically feasible and similar to cages already present in the literature, helping to increase the likelihood of the future synthetic realisation of these predictions. The presented approach is generalisable and can be tailored to target a wide range of properties in molecular material systems. Supramolecular chemistry ; Fullerene ; Nanotechnology ; Materials science ; Planar ; Computer science ; Molecule ; Chemistry Fullerene Chemistry and Applications ; Molecular Junctions and Nanostructures ; Boron and Carbon Nanomaterials Research
Authors: Sirilata Polepalli | Bhawna Uttam | Chebrolu P. Rao
Source: Materials Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Protein–inorganic nano hybrid sheets of Pd embedded BSA as a robust catalyst in water for oxidase mimic activity and C–C coupling reactions, and as a sustainable material for micromolar sensing of dopamine A new nano-hybrid sheet material using palladium and BSA protein (Pd_NP@BSA<sub>sheet</sub>) was developed as a sustainable catalyst for C–C coupling reactions in water, as an oxidase enzyme mimic and for the detection of dopamine in buffer, human serum and urine. Catalysis ; Chemistry ; Palladium ; Nano- ; Oxidase test ; Coupling reaction ; Glucose oxidase ; Combinatorial chemistry ; Enzyme ; Organic chemistry ; Chemical engineering Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Simoní Da Ros | Karen Aline Valter Flores | Marcio Schwaab | Elisa Barbosa-Coutinho | Nádia Regina Camargo Fernandes-Machado | José Carlos Pinto
Source: Journal of Industrial and Engineering Chemistry (article, 2020-11-27)
Field: Materials Science — Materials Chemistry

Phenomenological approaches for quantitative temperature-programmed reduction (TPR) and desorption (TPD) analysis Desorption ; Context (archaeology) ; Thermal desorption spectroscopy ; Characterization (materials science) ; Phenomenological model ; Biological system ; Reduction (mathematics) ; Representation (politics) ; Chemistry ; Experimental data ; Thermodynamics ; Temperature-programmed reduction ; Kinetics ; Materials science ; Catalysis ; Computer science ; Physical chemistry ; Mathematics ; Adsorption ; Nanotechnology ; Physics ; Statistics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies