jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jorge Alfredo Ardila‐Rey | Jesus Eduardo Ortíz | Werner Creixell | Firdaus Muhammad‐Sukki | Nurul Aini Bani
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks The measurement of partial discharges (PD) in electrical equipment or machines subjected to high voltage can be considered as one of the most important indicators when assessing the state of an insulation system. One of the main challenges in monitoring these degradation phenomena is to adequately measure a statistically significant number of signals from each of the sources acting on the asset under test. However, in industrial environments the presence of large amplitude noise sources or the simultaneous presence of multiple PD sources may limit the acquisition of the signals and therefore the final diagnosis of the equipment status may not be the most accurate. Although different procedures and separation and identification techniques have been implemented with very good results, not having a significant number of PD pulses associated with each source can limit the effectiveness of these procedures. Based on the above, this research proposes a new algorithm of artificial generation of PD based on a Deep Convolutional Generative Adversarial Networks (DCGAN) architecture which allows artificially generating different sources of PD from a small group of real PD, in order to complement those sources that during the measurement were poorly represented in terms of signals. According to the results obtained in different experiments, the temporal and spectral behavior of artificially generated PD sources proved to be similar to that of real experimentally obtained sources. Partial discharge ; Computer science ; Algorithm ; Limit (mathematics) ; Noise (video) ; Convolutional neural network ; Deep learning ; Generative adversarial network ; Identification (biology) ; Measure (data warehouse) ; Artificial intelligence ; Pattern recognition (psychology) ; Voltage ; Data mining ; Mathematics ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image and Signal Denoising Methods
Authors: Mikito Fujinami | Junji Seino | Hiromi Nakai
Source: Bulletin of the Chemical Society of Japan (article, 2020-03-12)
Field: Materials Science — Materials Chemistry

Quantum Chemical Reaction Prediction Method Based on Machine Learning Abstract A quantum chemical reaction prediction (QC-RP) method based on machine learning was developed to predict chemical products from given reactants. The descriptors contain atomic information in reactants such as charge, molecular structure, and atomic/molecular orbitals obtained by the quantum chemical calculations. The QC-RP method involves two procedures, namely, learning and prediction. The learning procedure constructs screening and ranking classifiers using 1625 polar and 95 radical reactions in a textbook of organic chemistry. In the prediction procedure, the screening classifier distinguishes reactive and unreactive atoms and the ranking one provides reactive atom pairs in ranking order. Numerical assessments confirmed the high accuracies both of the screening and ranking classifiers in the prediction procedures. Furthermore, an analysis on the classifiers unveiled important descriptors for the prediction. Chemistry ; Quantum chemical ; Quantum chemistry ; Molecular descriptor ; Artificial intelligence ; Ranking (information retrieval) ; Machine learning ; Chemical polarity ; Molecule ; Computational chemistry ; Computer science ; Quantitative structure–activity relationship ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Guibin Dong | Xiucheng Li | Jingxiao Zhao | Shuai Su | R.D.K. Misra | Ruoxiu Xiao | Chengjia Shang
Source: Materials Today Communications (article, 2020-06-12)
Field: Materials Science — Materials Chemistry

Machine learning guided methods in building chemical composition-hardenability model for wear-resistant steel Hardenability ; Materials science ; Metallurgy ; Chemical composition ; Composition (language) ; Organic chemistry ; Alloy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Daegun You | Shraddha Ganorkar | Sooran Kim | Keonwook Kang | Won-Yong Shin | Dongwoo Lee
Source: Scripta Materialia (article, 2020-03-13)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction models for formation energies of interstitial atoms in HCP crystals Kriging ; Support vector machine ; Parametric statistics ; Gaussian process ; Materials science ; Regression ; Linear regression ; Process (computing) ; Regression analysis ; Predictive modelling ; Gaussian ; Computer science ; Machine learning ; Biological system ; Artificial intelligence ; Algorithm ; Computational chemistry ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Ning Wang | Shiping Huang
Source: Physical review. B./Physical review. B (article, 2020-09-30)
Field: Materials Science — Materials Chemistry

Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential We introduce a machine-learning (ML) interatomic potential for Mg-H system based on Behler-Parrinello approach. In order to fit the complex bonding conditions in the cluster structure, we combine multiple sampling strategies to obtain training samples that contain a variety of local atomic environments. First-principles calculations based on density functional theory (DFT) are employed to get reference energies and forces for training the ML potential. For the calculation of bulk properties, phonon dispersion, gas-phase ${\mathrm{H}}_{2}$ interactions, and the potential energy surface (PES) for ${\mathrm{H}}_{2}$ dissociative adsorption on Mg(0001) surfaces, our ML potential has reached DFT accuracy at the level of GGA-PBE, and can be extended by combining the DFT-D3 method to describe van der Waals interaction. Moreover, through molecular dynamics (MD) simulations based on the ML potential, we find that for ${\mathrm{Mg}}_{n}{\mathrm{H}}_{m}$ clusters, $\mathrm{Mg}/\mathrm{Mg}{\mathrm{H}}_{x}$ phase separation occurs when $m&lt;2n$, and for a cluster with a diameter of about 4 nm, the Mg part of the cluster forms a hexagonal close-packed (hcp) nanocrystalline structure at low temperature. Also, the calculated diffusion coefficients reproduce the experimental values and confirm an Arrhenius type temperature dependence in the range of 400 to 700 K. Nanoclusters ; van der Waals force ; Molecular dynamics ; Cluster (spacecraft) ; Materials science ; Nanocrystalline material ; Interatomic potential ; Density functional theory ; Potential energy surface ; Chemical physics ; Atomic physics ; Physical chemistry ; Physics ; Ab initio ; Computational chemistry ; Nanotechnology ; Chemistry ; Molecule Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Juan Iván Gómez Peralta | Xim Bokhimi
Source: Journal of Solid State Chemistry (article, 2020-02-08)
Field: Materials Science — Materials Chemistry

Discovering new perovskites with artificial intelligence Electronegativity ; Artificial neural network ; Perovskite (structure) ; Atom (system on chip) ; Crystal structure ; Crystal (programming language) ; Quantum chemical ; Crystallography ; Materials science ; Feature (linguistics) ; Chemistry ; Artificial intelligence ; Chemical physics ; Computer science ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Jin-Woong Lee | Woon Bae Park | Byung Do Lee | Seonghwan Kim | Nam Hoon Goo | Kee‐Sun Sohn
Source: Scientific Reports (article, 2020-11-24)
Field: Materials Science — Materials Chemistry

Dirty engineering data-driven inverse prediction machine learning model Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). We also suggested a practical protocol to elucidate how to treat engineering data collected from industry, which is not prepared as independent and identically distributed (IID) random data. Inverse ; Computer science ; Dimension (graph theory) ; Autoencoder ; Set (abstract data type) ; Property (philosophy) ; Feature (linguistics) ; Feature engineering ; Inverse problem ; Data mining ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematical optimization ; Mathematics ; Artificial neural network ; Deep learning Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Introducing Machine Learning: Science and Technology Abstract Due to the remarkable progress of ever-growing digitalisation and computing capabilities, data has become increasingly abundant, and machine learning has emerged as a key ingredient in many enabling technologies within modern society. Its potential for pushing the frontiers of science is now also clear and has been demonstrated in various domains extending from novel materials design, quantum physics and the simulation of molecules and chemical systems, to particle physics, medical imaging, space science, climate science and drug discovery. Conceived in close consultation with the community, Machine Learning: Science and Technology has been launched as a unique multidisciplinary, open access journal that will bridge the application of machine learning across the natural sciences with new conceptual advances in machine learning methods as motivated by physical insights. Data science ; Computer science ; Bridge (graph theory) ; Artificial intelligence ; Multidisciplinary approach ; Learning sciences ; Chemical space ; Key (lock) ; Drug discovery ; Educational technology ; Sociology ; Chemistry ; Social science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Yu. G. Kabaldin | M. S. Anosov | Д. А. Шатагин
Source: IOP Conference Series Materials Science and Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of the mechanism of the destruction of metals based on approaches of artificial intelligence and fractal analysis Abstract The article examines modern informational approaches to assess the degree of damageability of materials based on their fractographic images. The possibility of using the fractal dimension, wavelet transform and convolutional artificial neural networks for tiling and classifying the share of viscous and brittle destructions on fractures is shown. The results of experimental studies of the impact viscosity of materials with different types of crystal lattices in a wide range of temperatures are presented. Fractal dimension ; Fractal ; Brittleness ; Mechanism (biology) ; Artificial neural network ; Convolutional neural network ; Artificial intelligence ; Wavelet transform ; Fractal analysis ; Materials science ; Biological system ; Wavelet ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Physics ; Metallurgy ; Mathematical analysis Material Properties and Failure Mechanisms ; Economic and Technological Systems Analysis ; Mining and Gasification Technologies
Authors: Arijit Chakraborty | Abhishek Sivaram | S. Lakshminarayanan | Venkat Venkatasubramanian
Source: Computers & Chemical Engineering (article, 2020-05-15)
Field: Materials Science — Materials Chemistry

Mechanism discovery and model identification using genetic feature extraction and statistical testing Mechanism (biology) ; Computer science ; Machine learning ; Artificial intelligence ; Identification (biology) ; Black box ; Simple (philosophy) ; Statistical model ; Data mining Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Ayana Ghosh | F. Ronning | Serge Nakhmanson | Jian‐Xin Zhu
Source: Physical Review Materials (article, 2020-06-19)
Field: Materials Science — Materials Chemistry

Machine learning study of magnetism in uranium-based compounds Actinide and lanthanide-based materials display exotic properties that originate from the presence of itinerant or localized $f$ electrons and include unconventional superconductivity and magnetism, hidden order, and heavy-fermion behavior. Due to the strongly correlated nature of the $5f$ electrons, magnetic properties of these compounds depend sensitively on applied magnetic field and pressure, as well as on chemical doping. However, precise connection between the structure and magnetism in actinide-based materials is currently unclear. In this investigation, we established such structure-property links by assembling and mining two datasets that aggregate, respectively, the results of high-throughput density functional theory simulations and experimental measurements for the families of uranium- and neptunium-based binary compounds. Various regression algorithms were utilized to identify correlations among accessible attributes (features or descriptors) of the material systems and predict their cation magnetic moments and general forms of magnetic ordering. Descriptors representing compound structural parameters and cation $f$-subshell occupation numbers were identified as most important for accurate predictions. The best machine learning model developed employs the random forest regression algorithm. It can predict both spin and orbit moment size with root-mean-square error of $0.17{\ensuremath{\mu}}_{\mathrm{B}}$ and $0.19{\ensuremath{\mu}}_{\mathrm{B}}$, respectively. The random forest classification algorithm is used to predict the ordering (paramagnetic, ferromagnetic, and antiferromagnetic) of such systems with 76% accuracy. Materials science ; Magnetism ; Uranium ; Metallurgy ; Condensed matter physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Rare-earth and actinide compounds
Authors: Suraj Shyam | Shivam Kaul | Nirav Kalsara | Narendiranath Babu Thamba
Source: Journal of Composite Materials (article, 2020-08-05)
Field: Materials Science — Polymers and Plastics

Mechanical behaviour and microscopic analysis of epoxy and E-glass reinforced banyan fibre composites with the application of artificial neural network and deep neural network for the automatic prediction of orientation This paper deals with the testing of tensile and flexural behaviour of epoxy-reinforced natural fibre composites, for which Banyan fibres have been selected as the natural fibre. Variations are made in the orientation of the fibres to determine which orientation made the composite the strongest. The fibre strands are arranged in different orientations, such as the uniaxial, biaxial and criss-cross arrangements, to differentiate between the orientations and observe which arrangement exhibited the best mechanical behaviour. The fibres are initially washed with 0.5% weight/volume (w/v) NaOH solution, following which specimens of the composites are made using wooden moulds designed according to ASTM standards. Biaxial layers of E-glass are incorporated into the matrix in an attempt to enhance the mechanical properties of the specimen. The variances observed in the Young’s modulus are analysed to understand the factors that majorly impacted it. For a better understanding of the results, the chemical functional groups and the microstructure of the samples are analysed with the aid of Fourier-Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM) and X-Ray powder Diffraction (XRD). Additionally, predictive models are simulated using Artificial and Deep Neural Networks to recognise patterns in the data, by varying specific parameters. The results obtained indicated that Banyan fibre composites can replace conventionally-used materials and serve real-world purposes better. Materials science ; Composite material ; Epoxy ; Fourier transform infrared spectroscopy ; Composite number ; Ultimate tensile strength ; Flexural strength ; Glass fiber ; Scanning electron microscope ; Optics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Mechanical Behavior of Composites
Authors: D.J. Silva | J. Ventura | João P. Araújo
Source: Energy and AI (article, 2020-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the performance of magnetocaloric systems using machine learning regressors Since refrigeration, air-conditioning and heat pump systems account to 25–30% of all energy consumed in the world, there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances. Magnetocaloric systems, i.e. refrigerators and heat pumps, are promising solutions due to their large theoretical Coefficient Of Performance (COP). However, there is still a long way to make such systems marketable. One barrier is the cost of the magnet and magnetocaloric materials, which can be overcome by decreasing the materials quantity, e.g. by optimizing the geometry with efficient dimensioning procedures. In this work, we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps: temperature span, heating power and COP. We used 4 different regressors: ordinary least squares, ridge, lasso and K-Nearest Neighbors (KNN). By using a dataset generated by numerical calculations, we have arrived at minimum average relative errors of the temperature span, heating power and COP of 23%, 29% and 31%, respectively. While the lasso regressor is more appropriate when using small datasets, the ordinary least squares regressor shows the best performance when using more samples. The best order of polynomials range between 3, for the heating power, to 5, for the COP. The worse performance in predicting the three performance values occurs when using the KNN regressor. Furthermore, the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values. Coefficient of performance ; Magnetic refrigeration ; Refrigeration ; Heat pump ; Lasso (programming language) ; Dimensioning ; Computer science ; Ordinary least squares ; Algorithm ; Machine learning ; Mathematics ; Mechanical engineering ; Heat exchanger ; Engineering ; Physics Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Jongmin Park | Hyo Seok Kim | Won Bo Lee | Myung‐June Park
Source: Catalysts (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

Trends and Outlook of Computational Chemistry and Microkinetic Modeling for Catalytic Synthesis of Methanol and DME The first-principle modeling of heterogeneous catalysts is a revolutionarily approach, as the electronic structure of a catalyst is closely related to its reactivity on the surface with reactant molecules. In the past, detailed reaction mechanisms could not be understood, however, computational chemistry has made it possible to analyze a specific elementary reaction of a reaction system. Microkinetic modeling is a powerful tool for investigating elementary reactions and reaction mechanisms for kinetics. Using a microkinetic model, the dominant pathways and rate-determining steps can be elucidated among the competitive reactions, and the effects of operating conditions on the reaction mechanisms can be determined. Therefore, the combination of computational chemistry and microkinetic modeling can significantly improve computational catalysis research. In this study, we reviewed the trends and outlook of this combination technique as applied to the catalytic synthesis of methanol (MeOH) and dimethyl ether (DME), whose detailed mechanisms are still controversial. Although the scope is limited to the catalytic synthesis of limited species, this study is expected to provide a foundation for future works in the field of catalysis research based on computational catalysis. Chemistry ; Catalysis ; Dimethyl ether ; Biochemical engineering ; Methanol ; Elementary reaction ; Reactivity (psychology) ; Computational chemistry ; Reaction mechanism ; Chemical reaction engineering ; Nanotechnology ; Kinetics ; Organic chemistry ; Materials science ; Physics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Jean‐Marc Noël | Jean‐François Lemineur
Source: Current Opinion in Electrochemistry (article, 2020-10-20)
Field: Chemistry — Electrochemistry

Optical microscopy to study single nanoparticles electrochemistry: From reaction to motion Nanoparticles (NPs)-based electrochemical devices are generating a growing interest and optical microscopy has recently proven to be a powerful tool to apprehend their electrochemical behavior. Through several striking examples, this review demonstrates how label-free optical imaging coupled to an electrochemical actuation can be used to probe operando the physical and electrochemical properties of single NPs, with high resolution and sensitivity and without additional emitters. Such an approach can be particularly relevant to establish clear structure-motion/reactivity relationships required to optimize NPs exploited as electrode materials. Electrochemistry ; Nanotechnology ; Microscopy ; Materials science ; Electrode ; Nanoparticle ; Optical microscope ; Resolution (logic) ; Chemistry ; Optics ; Computer science ; Scanning electron microscope ; Physics ; Composite material Electrochemical Analysis and Applications ; Force Microscopy Techniques and Applications ; Conducting polymers and applications