jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shan Zhu | Jiajun Li | Liying Ma | Chunnian He | Enzuo Liu | Fang He | Chunsheng Shi | Naiqin Zhao
Source: ChemRxiv (preprint, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Enabled Capacitance Prediction for Carbon-Based Supercapacitors Carbon is the most widely used electrode for the supercapacitors. To predict the capacitance of carbon-based supercapacitors, this work applies three machine learning (ML) methods, including linear regression, Lasso and artificial neural network. For training the ML process, we extracted data from hundreds of published papers. Moreover, five variables were selected to figure out their impact on capacitance, including specific surface area, calculated pore size, I&lt;sub&gt;D&lt;/sub&gt;/I&lt;sub&gt;G&lt;/sub&gt; ratio, N-doping level and voltage window. By evaluated with the real data, all of three methods achieve acceptable prediction results, and ANN exhibits the best performance. More importantly, this work shows the potential of ML in material science and advanced applications. Supercapacitor ; Capacitance ; Artificial neural network ; Carbon fibers ; Lasso (programming language) ; Materials science ; Work (physics) ; Voltage ; Computer science ; Nanotechnology ; Artificial intelligence ; Electrode ; Electrical engineering ; Chemistry ; Composite material ; Engineering ; Mechanical engineering Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Taylor D. Sparks | Steven Kauwe | Taylor Welker
Source: ChemRxiv (preprint, 2018-10-23)
Field: Materials Science — Materials Chemistry

Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning The field of materials science has seen an explosion in the amount of accessible high quality data. With this sudden surge of data, the application of machine learning (ML) onto materials data has led to great results. Particular success has been found in training models based on chemical formula. Such models have traditionally focused on learning from density functional theory (DFT) or experimental data. Though some researchers have explored the use of DFT calculated properties as features for learning, this has not gained much traction since the machine learning predictions would be limited by the DFT computation time and accuracy. In this work, we explore the use of a stacked ensemble learning system that combines machine learning from DFT calculations to improve learning on experimental data. This is accomplished by handling the DFT and experimental data separately, training distinct models for each. The DFT models are used to generate a "predicted DFT" value for the formulae in the experimental data. A meta-learner-trained using predictions generated by the experimental models combined with predictions from the DFT models-is shown to improve root-mean-squared-error by over 9% in the test data, when compared to a baseline model that only learns from the training data. Computer science ; Experimental data ; Machine learning ; Artificial intelligence ; Density functional theory ; Ensemble learning ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jianghao Liu | Shuang Du | Faliang Li | Haijun Zhang | Shaowei Zhang
Source: Journal of Wuhan University of Technology-Mater Sci Ed (article, 2018-10-01)
Field: Materials Science — Ceramics and Composites

Preparation of ZrB2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network Carbothermic reaction ; Materials science ; Raw material ; Decomposition ; Phase (matter) ; Carbon fibers ; Artificial neural network ; Chemical engineering ; Composite number ; Scanning electron microscope ; Composite material ; Computer science ; Chemistry ; Machine learning Advanced ceramic materials synthesis ; Advanced materials and composites ; Concrete and Cement Materials Research
Authors: Kamal Choudhary | Francesca Tavazza
Source: arXiv (Cornell University) (preprint, 2018-09-05)
Field: Materials Science — Materials Chemistry

Automatic convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in density functional theory In this work, we developed an automatic convergence procedure for k-points and plane wave cut-off in density functional (DFT) calculations and applied it to more than 25000 materials. The computational framework for automatic convergence can take a user-defined input as a convergence criterion. From the analysis of our results, we could relate k-point density and plane wave cut-off to material parameters such as density, bandgap, the slope of bands, number of band-crossings, the maximum plane-wave cut-off used in pseudopotential generation, crystal systems, and the number of unique species in materials. We also identified some material species that would require more careful convergence than others. Moreover, we statistically investigated the dependence of k-points and cutoff on exchange-correlation functionals and dimensionality (3D vs 2D) of materials. We utilized all this data to train machine learning models to predict the k-point line density and plane-wave cut-off for generalized materials. This would provide users with a good starting point towards converged DFT calculations. The code used, and the converged data are available on the JARVIS-DFT websites https://jarvis.nist.gov/ , and https://github.com/usnistgov/jarvis . Pseudopotential ; Convergence (economics) ; Plane wave ; Curse of dimensionality ; Density functional theory ; Cutoff ; Point (geometry) ; Plane (geometry) ; Algorithm ; Computer science ; Statistical physics ; Mathematics ; Physics ; Artificial intelligence ; Geometry ; Optics ; Condensed matter physics ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Hyeong‐Geun Yu | Jaehoon Lee | Dong-Jo Park | Nam Hyeon-Woo | Byeong-Hwang Park
Source:  (article, 2018-09-18)
Field: Chemistry — Analytical Chemistry

Efficient detection algorithm of chemical warfare agents for FTIR-based hyperspectral imagery using SVM classifier The hyperspectral imaging system (HIS) using a Fourier transform infrared (FTIR) spectrometer is one of the key technologies for detection and identification of chemical warfare agents (CWAs). Recently, various detection algorithms based on machine learning techniques have been studied. These algorithms are robust against performance degradation caused by noise signatures generated by FTIR instruments. However, interference signatures from background materials degrade detection performance. In this paper, we propose an efficient algorithm that uses a support vector machine (SVM) classifier to detect CWAs. In contrast to the conventional algorithms that use measured spectra to train the SVM classifier, the proposed algorithm trains the SVM classifier using CWA signatures obtained by removing background signatures from measured spectra. Therefore, the proposed algorithm is robust against the performance degradation induced by interference signatures from background materials. Experimental results verify that the algorithm can detect CWA clouds effectively. Hyperspectral imaging ; Support vector machine ; Computer science ; Artificial intelligence ; Algorithm ; Classifier (UML) ; Pattern recognition (psychology) ; Imaging spectrometer ; Statistical classification ; Spectrometer Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Gaoyin He | Fangfang Dai | Midong Shi | Qingsong Li | Yingmin Yu
Source: Russian Journal of Physical Chemistry A (article, 2018-04-17)
Field: Materials Science — Materials Chemistry

Hydrolysis of Methylal Catalyzed by Ion Exchange Resins in Aqueous Media In the present work, the chemical equilibrium and kinetics of methylal (PODE1) hydrolysis catalyzed by ion–exchange resin in aqueous solutions were investigated. The study covers temperatures between 333.15 and 363.15 K at various starting compositions covering (PODE1 + MeOH)/water molar ratio ranges from 0.5 to 1.5 in a time scale. On the basis of the experimental results, a mole fraction-based model of the chemical equilibrium and a pseudohomogeneous model are proposed to fit data based on true amount of monomeric formaldehyde. It has been demonstrated that the hydrolysis of PODE1 is slightly endothermic with the enthalpy 8.19 kJ/mol and the rate determining step. Finally, a feed–forward artificial neural networks (ANN) model is developed to model the concentration change of methanol in aqueous solutions. The results showed that the predicted data from designed ANN model were in good agreement with the experimental data with the coefficient (R2) of 0.98. Designed ANN provides a reliable method for modeling the hydrolysis reaction of methylal (PODE1). Hydrolysis ; Aqueous solution ; Formaldehyde ; Endothermic process ; Mole fraction ; Enthalpy ; Chemistry ; Catalysis ; Methanol ; Thermodynamics ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2018-08-20)
Field: Materials Science — Materials Chemistry

Machine learning predicts electron energies When chemists want to model the structural and electronic properties of atoms or molecules, they often turn to a computational technique called density functional theory (DFT). When DFT fails, chemists use approaches like coupled cluster (CC) or Møller–Plesset perturbation (MP2) theories. These generate more reliable values than DFT does, but they require thousands of times as much computational power as DFT, even for small molecules. Thomas F. Miller and colleagues at California Institute of Technology now demonstrate that machine learning might be the best of both worlds—as accurate as CC or MP2 and no more costly than DFT (J. Chem. Theory Comput. 2018, DOI: 10.1021/acs.jctc.8b00636). The researchers wanted to predict electronic structure correlation energies—a measure of interactions between electrons that helps chemists model how a molecule behaves. Their machine-learning approach predicts these values based on a set of known data. Miller’s group trained its algorithm on localized molecular orbitals of Electron ; Artificial intelligence ; Materials science ; Computer science ; Physics ; Nuclear physics Machine Learning in Materials Science
Authors: William F. Schneider | Hua Guo
Source: The Journal of Physical Chemistry C (article, 2018-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning ADVERTISEMENT RETURN TO ISSUEEditorialNEXTMachine LearningWilliam F. Schneider and Hua GuoCite this: J. Phys. Chem. C 2018, 122, 4, 1889Publication Date (Web):February 1, 2018Publication History Published online1 February 2018Published inissue 1 February 2018https://pubs.acs.org/doi/10.1021/acs.jpcc.8b00036https://doi.org/10.1021/acs.jpcc.8b00036editorialACS PublicationsCopyright © 2018 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1535Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (233 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Machine learning,Neural networks,Physical chemistry Get e-Alerts Computer science ; Artificial intelligence Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Source: Global NEST Journal (article, 2018-01-23)
Field: Materials Science — Biomaterials

Ultrasound–assisted adsorption of toxic dyes by cottonseed cake: artificial neural networks, regression models and response surface optimization In this study, dehydrated cottonseed cake as a low-cost and abundant byproduct in Turkey was utilized as an adsorbent for the decolorization of Reactive Blue 19 (RB19) and Reactive Yellow 145 (RY145) from aqueous solutions based on adsorption and ultrasound-assisted adsorption (UAA). Decolorization efficiency was optimized as a function of changes in process type, initial pH value, adsorbent concentration, temperature, reaction time, and initial dye concentrations of RY145 and RB19 based on response surface methodology (RSM) using Box-Behnken Design. The maximum decolorization efficiency of 99.9% for both RY145 and RB19 was obtained with ultrasound-assisted adsorption under the RSM-optimized conditions (with unity desirability) of 76.98 and 79.40 min reaction times, 233.20 and 254.29 mg L-1 initial dye concentrations, 1.37 and 1.44 g L-1 adsorbent concentrations, and 35.42 and 49.37 °C, respectively. The best-fit multiple non-linear regression models of decolorization efficiency with the highest adjusted coefficients of determination (R2adj) explained 99.52% and 99.48% of variations through adsorption of RY145 and RB19 and 98.14% and 98.01% of variations through UAA of RY145 and RB19, respectively, while artificial neural networks accounted for 99.82%. Response surface methodology ; Adsorption ; Chemistry ; Aqueous solution ; Cottonseed ; Chromatography ; Box–Behnken design ; Nuclear chemistry ; Organic chemistry ; Food science Electrospun Nanofibers in Biomedical Applications ; Dye analysis and toxicity ; Dyeing and Modifying Textile Fibers
Authors: Kamyar Movagharnejad | Niousha Vafaei
Source: SHILAP Revista de lepidopterología (article, 2018-12-01)
Field: Chemistry — Analytical Chemistry

A Comparison Between GA and PSO Algorithms in Training ANN to Predict the Refractive Index of Binary Liquid Solutions A total of 1099 data points consisting of alcohol-alcohol, alcohol-alkane, alkane-alkane, alcohol-amine and acid-acid binary solutions were collected from scientific literature to develop an appropriate artificial neural network (ANN) model. Temperature, molecular weight of the pure components, mole fraction of one component and the structural groups of the components were used as input parameters of the network while the refractive index was selected as its output. The ANN was optimized once by genetic algorithm (GA) and once again by particle swarm optimization algorithm (PSO) in order to predict the refractive index of binary solutions. The optimal topology of the ANN-GA consisted of 13 neurons in the hidden layer and the optimal topology of the ANN-PSO consisted of 16 neurons in the hidden layer. The results revealed that the ANN optimized by PSO had a better accuracy (MSE=0.003441 for test data) compared to the ANN optimized with GA (MSE=0.005117 for test data). Particle swarm optimization ; Binary number ; Artificial neural network ; Refractive index ; Genetic algorithm ; Algorithm ; Backpropagation ; Alkane ; Mole fraction ; Biological system ; Mathematics ; Topology (electrical circuits) ; Computer science ; Materials science ; Artificial intelligence ; Mathematical optimization ; Chemistry ; Physical chemistry ; Combinatorics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Chromatography
Authors: Paulina Dučkić | Robert B. Hayes
Source: Health Physics (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Total Ambient Dose Equivalent Buildup Factors for Portland Concrete In this work, total ambient dose equivalent buildup factors for Portland concrete slabs are calculated using Monte Carlo n-particle software MCNP6™. Buildup factor calculations could approach intractable solutions in general as they depend on a large number of variables. These include geometry, source energy, and the composition of the shield (which itself can be heterogeneous). In this work, Cf and americium-beryllium sources are considered, as well as monoenergetic incident neutrons in the energy range from 0.025 eV to 14 MeV at multiple incident angles. The shielding material of interest was taken to be standard Portland concrete. The transmitted neutron and gamma-ray ambient dose rate was calculated first and then used for total buildup factor calculations. Perhaps more telling than the calculated theoretical buildup factor, the credible dispersion in expected resultant buildup factors was also calculated by conducting a very rudimentary sensitivity analysis, varying the water content in the first case and then varying the amount of aggregate. An additional aim of this work is to provide a model based on the machine-learning technique called the support vector regression method in the calculation of concrete buildup factors. Equivalent dose ; Beryllium ; Electromagnetic shielding ; Americium ; Monte Carlo method ; Neutron ; Range (aeronautics) ; Work (physics) ; Neutron transport ; Materials science ; Nuclear engineering ; Environmental science ; Computational physics ; Actinide ; Nuclear physics ; Physics ; Statistics ; Mathematics ; Thermodynamics ; Engineering Graphite, nuclear technology, radiation studies ; Radiation Shielding Materials Analysis ; Nuclear Physics and Applications
Authors: Paulina Dučkić | Robert B. Hayes
Source: Health Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Total Ambient Dose Equivalent Buildup Factor Determination for Nbs04 Concrete Buildup factors are dimensionless multiplicative factors required by the point kernel method to account for scattered radiation through a shielding material. The accuracy of the point kernel method is strongly affected by the correspondence of analyzed parameters to experimental configurations, which is attempted to be simplified here. The point kernel method has not been found to have widespread practical use for neutron shielding calculations due to the complex neutron transport behavior through shielding materials (i.e. the variety of interaction mechanisms that neutrons may undergo while traversing the shield) as well as non-linear neutron total cross section energy dependence. In this work, total ambient dose buildup factors for NBS04 concrete are calculated in terms of neutron and secondary gamma ray transmission factors. The neutron and secondary gamma ray transmission factors are calculated using MCNP6™ code with updated cross sections. Both transmission factors and buildup factors are given in a tabulated form. Practical use of neutron transmission and buildup factors warrants rigorously calculated results with all associated uncertainties. In this work, sensitivity analysis of neutron transmission factors and total buildup factors with varying water content has been conducted. The analysis showed significant impact of varying water content in concrete on both neutron transmission factors and total buildup factors. Finally, support vector regression, a machine learning technique, has been engaged to make a model based on the calculated data for calculation of the buildup factors. The developed model can predict most of the data with 20% relative error. Neutron ; Neutron transport ; Electromagnetic shielding ; Transmission (telecommunications) ; Equivalent dose ; Dimensionless quantity ; Neutron temperature ; Computational physics ; Work (physics) ; Physics ; Nuclear physics ; Mechanics ; Computer science Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications
Authors: Emre Ardıç | Yakup Genç
Source:  (article, 2018-05-01)
Field: Chemistry — Analytical Chemistry

Classification of 1D signals using deep neural networks The absorbance spectrum technique is as old as the first alchemists. They sought to identify and understand their elixirs by looking at the color and opacity of solutions as different reagents were mixed, heated, and stirred. Today it remains the most widely used spectroscopic technique for studying liquids and gases due to its simplicity, accuracy, and ease of use. An absorbance spectrum can be used to identify substances or measure the concentration of a molecule in solution. In this work, PLSR, GBR, RF and CNN models are trained on absorbance spectrum data of different liquid solvents and concentration of a specific molecule is predicted. A large data set is collected to train and test the models. The proposed deep convolutional neural networks gave the best results. Absorbance ; Convolutional neural network ; Opacity ; Artificial neural network ; Biological system ; Computer science ; Data set ; Pattern recognition (psychology) ; Set (abstract data type) ; Near-infrared spectroscopy ; Test set ; Artificial intelligence ; Measure (data warehouse) ; Molecule ; Reagent ; Chemistry ; Materials science ; Chromatography ; Data mining ; Optics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Abd El-Aziz Hassanin Konsowa
Source: American Journal of Chemical Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

The Effect of Graphene/TIO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Nanomaterials on Photocatalytic Performance for Industrial Wastewater Treatment The aim of the present work is to combine TiO<sub>2</sub>/Graphene to increase photo-catalytic activity and obtain efficient removal of direct red 23 azo dye without difficult synthesis. Many operating variables which affect the process and some design aspects were studied. Reactor geometry is the main design parameter where slurry rectangular reactor and bubble column were compared with respect to hydrodynamic regimes, overall degradation efficiency and applicability in industrial scale. The removal rate was found to increase with increase in TiO<sub>2</sub> concentration, approaching a limiting value at catalyst loads of 3g/L. For rectangular reactor only 0.005g/L of rGO able to enhance the activity of photo-catalysis. However, the optimum concentration of rGO is decreased in the bubble column reactor to be 0.001g/L. In both reactor systems, photocatalytic activity increase significantly by decreasing wavelength of the irradiated lamp from 365 nm to 254 nm. Also, results show the ability of bubble column reactor to treat high concentrations of dye up to 200 ppm. That makes it suitable to be integrated with biological system to convert non-biodegradable contaminates into biodegradable organics. That reduces the operating cost of the process and makes it more applicable in industrial scale. Rectangular reactor has the ability to be modified to use sunlight instead of artificial lamps because of high surface exposed to sunlight. In general the performance of bubble column reactor was better than rectangular reactor because it has excellent mass transfer characteristics, which enhance the efficiency of the process. Bubble column reactor ; Photocatalysis ; Catalysis ; Materials science ; Nanometre ; Slurry ; Bubble ; Wastewater ; Chemical engineering ; Graphene ; Degradation (telecommunications) ; Irradiation ; Chemistry ; Nanotechnology ; Waste management ; Composite material ; Organic chemistry ; Electrical engineering Advanced Nanomaterials in Catalysis