jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junjie Hu | Mu Li | Peng Gao
Source: arXiv (Cornell University) (preprint, 2019-10-20)
Field: Materials Science — Materials Chemistry

MATGANIP: Learning to Discover the Structure-Property Relationship in Perovskites with Generative Adversarial Networks Accelerating the design of materials with artificial neural network draws more attention due to its magnitude potential. In the past works, some tools of materials information have been developed to promote the industrialize of state-of-the-art materials, such as the materials project, AFlow, and open quantum materials database. Else, more various endeavors are required for artificial general intelligence in the area of materials. In our works, we design neural networks named MATGANIP, which applies a combination of generative adversarial networks, graph networks, convolutional neural networks, and long short term memory for the perovskite materials. We adopt it for the building of a structure-property relationship, where the trained properties contain: the computational geometric property, tolerance factor; and the ground state property of Quantum theory, the vacuum energy. Moreover, the data-set about the ABX3 perovskites is used for the learning of tolerance factor, to extend its function of tolerance factor into the structural identification of the arrangement of disorder atoms; another data-set about the density functional theory (DFT) calculation results is for the ground state energy of quantum theory, to obtain the more accurate result than the training of DFT calculated results in similar works. In our training task of the DFT date-set, we adopt the intuitive criterion to evaluate its performance: the mean absolute error (MAE) is smaller than 0.3 meV/atom, and the mean absolute error rate (MAER) is smaller than 0.01%. These results prove the potential ability of our MATGANIP in developing the relationship between materials structure and their properties. Notably, it suits for some scenes involved massive possible structures and their properties, such as the arrangement of the atoms in the structural phase transition of the inorganic perovskites with mixed atoms. Computer science ; Generative grammar ; Density functional theory ; Convolutional neural network ; Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Quantum ; Theoretical computer science ; Algorithm ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Eric S. Harper | Meghan N. Weber | Matthew S. Mills
Source: 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) (article, 2019-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Accelerated Nano-Targeted Inhomogeneous Structures Optical metamaterials can achieve optical performance specifications otherwise unattainable compared to their bulk constituents via the use of skillfully engineered nanoscale features. These features function as sub-wavelength optical scatterers, causing incident light to interact with metamaterials in a manner which is crucially dependent on their geometry. To design the next generation of functional materials, scientists and engineers must realize new methods which prudently explore the near-infinite number of possible metamaterial arrangements. We present a general methodology which harnesses machine learning to accelerate the process of metamaterial design and apply it specifically here, as a test case, to a silicon on insulator (SOI) reflective metasurface consisting of an array of nano-pillars. In particular, we implement artificial neural networks (ANNs) to solve the inverse design problem; i.e. we prescribe a desired reflection profile and utilize ANNs to advise what metasurface array of SOI nano-pillars gives the closest result. Utilizing a synthetic data set of devices simulated with rigorous coupled wave analysis (RCWA), we create an ANN-accelerated simulator, achieving a computational speedup of O(10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ) over the relatively quick RCWA simulation method. We then couple this simulator with an ANN-based predictor, which directly binds the SOI metasurfaces in question to a targeted optical performance. Together, this simulator/predictor ANNs combination provides a general framework in rapidly evaluating and designing potential optical metamaterial devices beyond what is currently possible via straightforward simulation and more standard optimization methods. Metamaterial ; Computer science ; Silicon on insulator ; Speedup ; Artificial neural network ; Artificial intelligence ; Physics ; Optoelectronics ; Silicon ; Parallel computing Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Zachary del Rosario | Matthias Rupp | Yoolhee Kim | Erin Antono | Julia Ling
Source: arXiv (Cornell University) (preprint, 2019-11-06)
Field: Materials Science — Materials Chemistry

Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of \emph{Pareto shell-scope error} to help judge the suitability of a model for proposing material candidates. Further, through synthetic cases and a thermoelectric dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for acquisition function design. Scope (computer science) ; Computer science ; Pareto principle ; Fidelity ; Machine learning ; Function (biology) ; Artificial intelligence ; Active learning (machine learning) ; Quality (philosophy) ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Lucy Ngo | Anton D. Nathanson | Tomasz Garbowski | Ulf Knothe | D. Zeidler | Melissa L. Knothe Tate
Source: BIO-PROTOCOL (article, 2019-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Electron Microscopy Sample Preparation Protocol Enabling Nano-to-mesoscopic Mapping of Cellular Connectomes and Their Habitats in Human Tissues and Organs Multibeam scanning electron microscopy (multiSEM) provides a technical platform for seamless nano-to-mesoscale mapping of cells in human tissues and organs, which is a major new initiative of the U.S. National Institutes of Health. Such cross-length-scale imaging is expected to provide unprecedented understanding of relationships between cellular health and tissue-organ as well as organismal-scale health outcomes. For example, understanding relationships between loss in cell viability and cell network connectivity enables identification of emergent behaviors and prediction of degenerative disease onset, in organs as diverse as bone and brain, at early timepoints, providing a basis for future treatments and prevention. Developed for rapid throughput imaging of minute defects on semiconductor wafers, multiSEM has recently been adapted for imaging of human organs, their constituent tissues, and their respective cellular inhabitants. Through integration of geospatial approaches, statistical and network modelling, advances in computing and the management of immense datasets, as well as recent developments in machine learning that enable the automation of big data analyses, multiSEM and other cross- cutting imaging technologies have the potential to exert a profound impact on elucidation of disease mechanisms, translating to improvements in human health. Here we provide a protocol for acquisition and preparation of sample specimen sizes of diagnostic relevance for human anatomy and physiology. We discuss challenges and opportunities to integrate this approach with multibeam scanning electron microscopy workflows as well as multiple imaging modalities for mapping of organ and tissue structure and function. Workflow ; Computer science ; Identification (biology) ; Protocol (science) ; Data science ; Nanotechnology ; Artificial intelligence ; Computational biology ; Biology ; Pathology ; Medicine ; Materials science ; Ecology Electron and X-Ray Spectroscopy Techniques ; Health, Environment, Cognitive Aging ; Advanced Electron Microscopy Techniques and Applications
Authors: Nurul Rawaida Ain Burhani | Masdi Muhammad | Nurfatihah Syalwiah Rosli
Source: Sustainability (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Combined Experimental and Field Data Sources in a Prediction Model for Corrosion Rate under Insulation Corrosion under insulation (CUI) is one of the increasing industrial problems, especially in chemical plants that have been running for an extended time. Prediction modeling, which is one of the solutions for this issue, has attracted increasing attention and has been considered for several industrial applications. The main objective of this work was to investigate the effect of combined data input in prediction modeling, which could be applied to improve the existing CUI rate prediction model. Experimental data and field historical data were gathered and simulated using an artificial neural network separately. To analyze the effect of data sources on the final corrosion rate under the insulation prediction model, both sources of data from experiment and field data were then combined and simulated again using an artificial neural network. Results exhibited the advantages of combined input data type from the experiment and field in the final prediction model. The model developed clearly shows the occurrence of corrosion by phases, which are uniform corrosion at the early phases and pitting corrosion at the later phases. The prediction model will enable better mitigation actions in preventing loss of containment due to CUI, which in turn will improve overall sustainability of the plant. Artificial neural network ; Corrosion ; Predictive modelling ; Field (mathematics) ; Experimental data ; Work (physics) ; Engineering ; Computer science ; Data mining ; Machine learning ; Materials science ; Mechanical engineering ; Metallurgy ; Mathematics ; Statistics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Danka Dragojlović | Lato Pezo | Dušica Čolović | Strahinja Vidosavljević | Milada Pezo | Radmilo Čolović | Bojana Kokić | Olivera Djuragic
Source: Acta periodica technologica (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Application of soybean oil and glycerol in animal feed production, ANN model In the past few decades the diet preparation in feed production has evolved towards more complicated technological operations, which include different liquid addition. A wide scale of different liquids is used in contemporary animal feed production, from oils and glycerol to more expensive products in a liquid form, such as enzymes, flavourings, amino acids, vitamins and others. In the presented study the liquid addition in feed production was observed, with a specific goal to investigate the spraying systems in order to better understand the effects of fluids, such as soybean oil and glycerol, on feed production. The dispersion angles of spraying nozzle for glycerol and soybean oil were determined as an indicator of the uniform application of liquids during feed production. Dispersion of the material was accomplished using the two-fluid nozzle. The performance of Artificial Neural Network (ANN) was compared with experimental data in order to develop rapid and accurate method for prediction of dispersion angle. The ANN model showed high prediction accuracy (r2 = 0.945). Glycerol ; Soybean oil ; Dispersion (optics) ; Nozzle ; Pulp and paper industry ; Artificial neural network ; Production (economics) ; Food science ; Biotechnology ; Biological system ; Chemistry ; Materials science ; Process engineering ; Mathematics ; Chemical engineering ; Chromatography ; Computer science ; Biology ; Biochemistry ; Engineering ; Artificial intelligence ; Mechanical engineering ; Physics Spectroscopy and Chemometric Analyses ; Plant Surface Properties and Treatments ; Microencapsulation and Drying Processes
Authors: F. Sedaghat | F. Yousefi | Hamideh Zolfaghari
Source: Journal of Engineering Thermophysics (article, 2019-04-01)
Field: Chemistry — Analytical Chemistry

Experimental Investigation and Modeling of S,N-GQDs Nanofluid Density Using New Equation of State and Artificial Neural Network Density measurement was performed on S,N-GQDs nanoparticles dispersed in base fluids such as water, ethylene glycol and 60:40 water/ethylene glycol. Firstly, we synthesized S,N-GQDs nanoparticles by the chemical vapor deposition method. UV-Vis absorption spectrometry, XRD, FT-IR, EDX and SEM analysis were done to elucidate the structure and morphology of the as prepared samples. The measurements were performed with different weight percentages from 0.09 to 0.9%. The temperature range of the measurements ranged from 20° to 60° C. Also, artificial neural network method and improved Tao-Mason equation of state (TM EOS) were used to calculate the density of nanofluids. These models are in good agreement with experimental results. Nanofluid ; Ethylene glycol ; Materials science ; Nanoparticle ; Equation of state ; Artificial neural network ; Analytical Chemistry (journal) ; Atmospheric temperature range ; Absorption (acoustics) ; Chemical engineering ; Nanotechnology ; Thermodynamics ; Chromatography ; Composite material ; Chemistry ; Physics ; Machine learning Petroleum Processing and Analysis ; Power Transformer Diagnostics and Insulation ; Fault Detection and Control Systems
Authors: Qiyuan Guan | Kang Guo | Weihong Tan | Yonghong Zhou
Source: Journal of Bioresources and Bioproducts (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

Rapid Decomposition of Epoxy Resins via Raman Spectrometry in Combination with Machine Learning Algorithms Epoxy resins are a group of important materials that have been used everywhere, and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has drawn great focus from scientists and engineers. By reacting different kinds of epoxy adhesives and curatives, massive kinds of epoxy resins with different characteristics are produced. Determination of original mixing ratio of epoxy adhesives and corresponding curatives of their curing products is useful in controlling and examining these materials. Here in this work, we described an efficient method based on Raman spectrometry and machine learning algorithms for rapid molar composition determination of epoxy resins. Original mixing ratio of epoxy adhesives and curatives could be calculated simply via Raman spectra of the products. Raman spectral data scanned during curing procedure was fed to random forest (RF) classification to calculate weights of Raman shift features and reduce data dimensionality, then spectral data of selected features were processed by partial least squares regression (PLSR) for model training and composition ratio determination. It turned out that ratio predictions of our model fit well to their actual values, with a coefficient of determination (R2) of 0.9926, and a root mean squared error (RMSE) of 0.0305. Epoxy ; Raman spectroscopy ; Adhesive ; Curing (chemistry) ; Materials science ; Algorithm ; Mean squared error ; Coefficient of determination ; Partial least squares regression ; Composite material ; Computer science ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Machine learning ; Chromatography ; Statistics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tanujit Chakraborty | Ashis Kumar Chakraborty | Swarup Chattopadhyay
Source: Journal of Computational and Applied Mathematics (preprint, 2019-05-28)
Field: Chemistry — Analytical Chemistry

A novel distribution-free hybrid regression model for manufacturing process efficiency improvement Process (computing) ; Computer science ; Artificial neural network ; Consistency (knowledge bases) ; Work (physics) ; Product (mathematics) ; Process engineering ; Regression ; Mathematical optimization ; Industrial engineering ; Engineering ; Artificial intelligence ; Mathematics ; Mechanical engineering ; Statistics Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Mineral Processing and Grinding
Authors: Bjorn Bottcher | Ali Sinai | Matthias Menge | Thomas Gräf | Ronald Plath | T. Hucker
Source: 2019 2nd International Conference on High Voltage Engineering and Power Systems (ICHVEPS) (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Algorithms for a Multi-Sensor Partial Discharge Expert System Applied to Medium Voltage Cable Connectors Partial discharge (PD) activity is a good indicator of the integrity of insulating materials. PDs emit high-frequency electromagnetic signals in the form of stochastic pulse sequences and can be discriminated from background noise signals employing pattern recognition and deep learning algorithms. Until now, common PD detection methods are based on one channel measurements, e.g., the conventional IEC 60270 PD detection method. This makes data acquisition susceptible to noise and external PD signals. To rate the operational risk, expert knowledge is required. To improve diagnosis performance, especially for on-site testing and monitoring, a non-conventional PD measurement system is developed utilizing multiple sensors which detect PDs in different frequency domains. In this paper, several classification algorithms are compared regarding their efficiency to identify the PD signals of different faulty cable connectors. Also, their ability to discriminate or tolerate external noise is investigated.Therefore, PD signals from row measurements of 10 kV cable connectors with known faults have been recorded. Four sensor types, i.e., IEC 60270-based, capacitive sensor, high frequency current transformer (HFCT), and an ohmic sensor are used for the measurements. A suitable algorithm for multi-sensor classification that increases the decision accuracy, compared to single-sensor classification, is discussed in detail. Finally, the 5-channel online monitoring system based on self-developed hardware and the proposed evaluation algorithm is presented. Partial discharge ; Capacitive sensing ; Noise (video) ; Computer science ; Electronic engineering ; Transformer ; Condition monitoring ; Voltage ; Engineering ; Artificial intelligence ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Pei‐En Chen | Wenxiang Xu | Nikhilesh Chawla | Yi Ren | Yang Jiao
Source: SSRN Electronic Journal (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Novel Hierarchical Correlation Functions for Quantitative Representation of Complex Heterogeneous Materials and Microstructural Evolution Representation (politics) ; Correlation ; Statistical physics ; Mathematics ; Computer science ; Physics ; Geometry Machine Learning in Materials Science
Authors: İjlal ŞİMŞEK
Source: El-Cezeri Fen ve Mühendislik Dergisi (article, 2019-09-28)
Field: Materials Science — Ceramics and Composites

Mekanik Alaşımlama Yöntemi ile Üretilen Farklı Miktarlarda ZrO2 Takviyeli Al-2Gr Matrisli Kompozit Malzemelerin Aşınma Performanslarının İncelenmesi Bu çalışmada, mekanik alaşımlama yöntemi ile Al-2Gr matrise farklı miktarlarda ZrO 2 ilave edilerek üretilen kompozit malzemelerin farklı yükler altında aşınma performansı incelenmiştir. Alüminyum matrise hacimce %2 grafit ilave edildikten sonra, 4 farklı miktarda (%3, %6, %9 ve %12) ZrO 2 ilave edilmiştir. 60 dakika süre ile mekanik alaşımlanan kompozit tozlar, 700 MPa basınçla soğuk preslenerek green kompaktlar üretilmiştir. Üretilen green kompaktlar 600 °C sıcaklıkta 2 saat sinterlenmiştir. Sinterlenen ZrO 2 takviyeli alüminyum kompozit malzemeler, taramalı elektron mikroskobu, X-ışın kırınımı, sertlik ve yoğunluk ölçümü ile karakterize edilmiştir. Aşınma testleri standart blok on ring aşınma test cihazında 3 farklı yük altında (5, 10 ve 20 N), 0,6 ms -1 kayma hızı ve üç farklı (53, 72 ve 94 m) kayma mesafesi kullanılmıştır. Yapılan çalışmalar sonucunda, kompozit malzemelerin içerisinde artan ZrO 2 miktarı ile sertlik ve yoğunlukları artmaktadır. Aşınma testleri sonucunda, en yüksek ağırlık kaybı, takviyesiz Al-2Gr matris alaşımda elde edilirken, en düşük ağırlık kaybı ise %12 ZrO 2 ilaveli kompozit malzemelerde elde edilmiştir. Bununla birlikte artan takviye miktarı ile sürtünme katsayısında ise azalma olduğu görülmüştür. Physics ; Chemistry Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Aluminum Alloys Composites Properties
Authors: N. Castin | G. Bonny | A. Bakaev | F. Bergner | C. Domain | J.M. Hyde | Luca Messina | B. Radiguet | L. Malerba
Source: SSRN Electronic Journal (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The Dominating Mechanisms for the Formation of Solute-Rich Clusters in Steels under Irradiation Irradiation ; Chemistry ; Materials science ; Physics ; Nuclear physics Nuclear Materials and Properties ; Fusion materials and technologies ; Ion-surface interactions and analysis
Authors: Mahdi Nouri | Kamran Ghasemzadeh | Adolfo Iulianelli
Source: Membranes (article, 2019-08-27)
Field: Materials Science — Materials Chemistry

Theoretical Evaluation of Graphene Membrane Performance for Hydrogen Separation Using Molecular Dynamic Simulation The main purposes of this study are to evaluate the performance of graphene membranes in the separation/purification of hydrogen from nitrogen from a theoretical point of view using the molecular dynamic (MD) simulation method, and to present details about molecular mechanisms of selective gas diffusion through nanoscale pores of graphene membranes at the simulated set conditions. On the other hand, permeance and perm-selectivity are two significant parameters of such a membrane that can be controlled by several variables such as pressure gradient, pore density, pore layer angles etc. Hence, in this work, the hydrogen and nitrogen permeating fluxes as well as the H<sub>2</sub>/N<sub>2</sub> ideal perm-selectivity are investigated from a theoretical point of view in a two-layer nanoporous graphene (NPG) membrane through classical MD simulations, wherein the effects of pressure gradient, pore density, and pore angle on the NPG membrane performance are evaluated and discussed. Simulation outcomes suggest that hydrogen and nitrogen permeating fluxes increase as a consequence of an increment of pressure gradient across the membrane and pore density. Membrane ; Permeance ; Molecular dynamics ; Graphene ; Materials science ; Nanoporous ; Hydrogen ; Selectivity ; Gas separation ; Chemical physics ; Chemical engineering ; Nitrogen ; Diffusion ; Nanotechnology ; Permeation ; Chemistry ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Physics Graphene research and applications ; Membrane Separation and Gas Transport ; Muon and positron interactions and applications