jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan Wang | Kesheng Wu | Alex Sim | Seongwook Hwangbo
Source: Preprints.org (preprint, 2022-12-20)
Field: Materials Science — Materials Chemistry

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors. Partial discharge ; Computer science ; Ultra high frequency ; Transformer ; Algorithm ; Real-time computing ; Engineering ; Electrical engineering ; Telecommunications ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization
Authors: Richeliel Albert Rodrigues Silva | Luciano Farinha Watzlawick | Henrique Soares Koehler | Joelmir Augustinho Mazon | Ramon de Sousa Leite
Source: FLORESTA (article, 2022-10-07)
Field: Chemistry — Organic Chemistry

Species associated with Curitiba prismatica (D. Legrand) Salywon &amp; Landrum in a Faxinal system Species associated with Curitiba prismatica (D. Legrand) Salywon &amp; Landrum in a Faxinal system. Curitiba prismatica, belonging to the family Myrtaceae, is an endemic species of Araucaria mixed forest (AMF) areas whose leaf essential oils have pharmaceutical properties. The objective of this study was to describe the floristic aspects, diversity, competition and diametric transition of the species associated with C. prismatica in an Araucaria mixed forest area exploited in the Faxinal system. A survey of 35 sample units was carried out using the Prodan method, classifying them as initial (pioneer and early secondary) and late (old secondary and climax). Two hundred ten trees were observed, divided into 16 botanical families, 23 genera and 29 species, with the most associated species being: C. prismatica, Casearia oblique, Casearia sylvestris, Cinnamodendron dinisii (30. Ocotea odorifera, and Casearia decandra. The Shannon (H') and Simpson (C) indices of the associated species were 2.54 nats.ind-1 and 0.14 nats.ind-1, respectively. According to the competition rates denoted by the indices of basal area (BAL), Glover and Hool (IGH) and Hegyi, competition of C. prismatica occurred with greater intensity in trees with DBH ≤ 25 cm. The results provide a better understanding of the ecological behavior of species associated with C. prismatica in AMF areas subject to Faxinal systems. Araucaria ; Competition (biology) ; Curitiba ; Basal area ; Floristics ; Geography ; Biology ; Ecology ; Forestry ; Botany ; Species richness ; Humanities Plant-Derived Bioactive Compounds ; Phytochemistry Medicinal Plant Applications ; African Botany and Ecology Studies
Authors: Sermet MİR | Barış YILDIRIM | Murat Kurt
Source: Deu Muhendislik Fakultesi Fen ve Muhendislik (article, 2022-09-19)
Field: Materials Science — Polymers and Plastics

Çok Katmanlı Malzemelerde Açısal Renk Değişimleri ve Işıltı Etkilerinin Analizi Çok katmanlı malzemeler, her bir katmanı farklı bir saçılım davranışına sahip olabilen ve katmanları içerisinde anizotropik davranışa sahip bileşenler barındıran malzeme yapılarıdır. Çok katmanlı malzemeler kozmetik, mücevher ve araba boyası endüstrilerinde yoğun olarak kullanılmaktadır. Ayrıca, bir çok gerçek dünya malzemesi kompozisyon içeriğinde açısal renk değişimi (goniochromatic) ve ışıltı (sparkle) etkilerine neden olan mikroskobik parçacıklar içerir. Bu sebeple, malzemenin gerçekçi görselleştirmesini sağlamak için malzemenin doğru temsil edilmesi büyük önem arz etmektedir ve bu doğrultuda bir çok model önerilmiştir. Bu makalede, çok katmanlı modern araba boyası malzemesi baz alınarak, malzemenin üzerinde görülen açısal renk değişimleri ve ışıltı etkilerini modelleyebilen çalışmaların analizi yapılmış ve deneysel karşılaştırmalar ile modellerin örnek malzemeleri doğru temsil etme kapasiteleri karşılaştırılmıştır. Ek olarak, çalışmamız içerisinde modellerin performans değerleri de karşılaştırılarak deneysel çalışmalarımız içerisinde bu bilgiler paylaşılmıştır. En son kısımda ise bir tablo halinde analizi yapılan çalışmaları içerdikleri karmaşık özel etkileri özetleyerek, okuyucuların bilgisine sunmaktayız. Physics Textile materials and evaluations
Authors: L. M Stevanato | Edison Pignaton de Freitas
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Delimitação de unidades de gestão diferenciada para o cultivo da cana-de-açúcar na região de Barretos (São Paulo) a partir de índices espectrais e altimetria obtidos por sensoriamento remoto orbital A cana-de-açúcar tem forte influência na economia brasileira e demanda constantes investimentos em pesquisa de técnicas de agricultura de precisão. O objetivo deste trabalho foi aplicar algumas destas técnicas para delimitação de unidades de gestão diferenciada (UGDs), em uma área cultivada com cana-de-açúcar na região de Barretos (SP), utilizando como atributos dados de relevo e índices espectrais, obtidos a partir da SRTM e de imagens da missão Sentinel-2, respectivamente. Uma rotina em JavaScript foi desenvolvida e utilizada no ambiente interativo Code Editor da plataforma Google Earth Engine para obtenção e processamento destes dados, resultando em uma malha de pontos georreferenciados com os valores de cada atributo, sendo que para os índices espectrais, por se tratar de uma coleção de imagens com diferentes datas, o valor atribuído a cada ponto foi o maior para a posição e para o atributo. Tais malhas foram processadas para verificação das correlações e posterior formação de agrupamentos, a partir dos quais foram obtidas diferentes configurações de UGDs quanto aos níveis de alto, médio e baixo potencial produtivo, que foram analisadas graficamente a fim de identificar quais contemplavam, de forma satisfatória, os diversos atributos existentes e a viabilidade de manejo da área. Selecionada a configuração considerada mais adequada, sua representação gráfica foi editada para aprimoramento da delimitação final das UGDs. A metodologia proposta foi considerada adequada, possibilitando obter e processar dados atualizados em grande quantidade de forma rápida e gratuita, bem como a visualização de diferentes cenários, facilitando a tomada de decisão. Physics Spectroscopy and Chemometric Analyses
Authors: Shota Ono
Source: arXiv (Cornell University) (preprint, 2022-02-09)
Field: Materials Science — Materials Chemistry

Finding the stable structures of 2D hexagonal materials with Bayesian optimization: Beyond the structural relationship with 3D crystals in weakly-bonded binary systems The graphene-graphite relationship in structural geometry is a basic principle to predict novel two-dimensional (2D) materials. Here, we demonstrate that this is not the case in binary metallic systems. We use the Bayesian optimization framework combined with the density-functional theory approach to determine the stable configuration of atomic species on a hexagonal plane. We show that the optimized structure of 2D Cu-Au exhibits the hexagonal lattice of a hexagonal ring of Cu atoms containing one Au atom, where the number of the Cu atoms is larger than that of the Au atoms in the unit cell, which is difficult to speculate from the atomic distribution of CuAu in the L1$_0$ structure. We also show that 2D Cu-$X$ with $X=$ Be, Zn, and Pd have hexagonal or elongated rings containing different atoms in the unit cell. Based on the binary Lennard-Jones model, we propose that such structures can appear for weakly-bonded systems located in between the phase-separated and strongly-bonded systems with the interatomic interaction energy between different species. Materials science ; Binary number ; Hexagonal crystal system ; Atom (system on chip) ; Crystallography ; Graphene ; Lattice (music) ; Graphite ; Density functional theory ; Hexagonal phase ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Computer science ; Mathematics Graphene research and applications ; Machine Learning in Materials Science ; Chemical and Physical Properties of Materials
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case Bayesian probability ; Bayesian optimization ; Noise (video) ; Optimization problem ; Minification Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Optimal Experimental Design Methods
Authors: 信明 安尾 | Nobuaki Yasuo
Source: Institutional Repositories DataBase (IRDB) (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case Bayesian probability ; Bayesian optimization ; Noise (video) ; Optimization problem ; Minification Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Optimal Experimental Design Methods
Authors: Mika Sarvilahti | Lasse Laurson
Source: arXiv (Cornell University) (preprint, 2022-10-05)
Field: Materials Science — Materials Chemistry

Bayesian optimization of discrete dislocation plasticity of two-dimensional precipitation hardened crystals Discovering relationships between materials' microstructures and mechanical properties is a key goal of materials science. Here, we outline a strategy exploiting Bayesian optimization to efficiently search the multidimensional space of microstructures, defined here by the size distribution of precipitates (fixed impurities or inclusions acting as obstacles for dislocation motion) within a simple two-dimensional discrete dislocation dynamics model. The aim is to design a microstructure optimizing a given mechanical property, e.g., maximizing the expected value of shear stress for a given strain. The problem of finding the optimal discretized shape for a distribution involves a norm constraint, and we find that sampling the space of possible solutions should be done in a specific way in order to avoid convergence problems. To this end, we propose a general mathematical approach that can be used to generate trial solutions uniformly at random while enforcing an Euclidean norm constraint. Both equality and inequality constraints are considered. A simple technique can then be used to convert between Euclidean and other Lebesgue $p$-norm (the 1-norm in particular) constrained representations. Considering different dislocation-precipitate interaction potentials, we demonstrate the convergence of the algorithm to the optimal solution and discuss its possible extensions to the more complex and realistic case of three-dimensional dislocation systems where also the optimization of precipitate shapes could be considered. Norm (philosophy) ; Mathematical optimization ; Dislocation ; Mathematics ; Applied mathematics ; Materials science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Hiroshi Sawada | Yoshitaka Taniyasu | Hideki Yamamoto
Source: npj Computational Materials (article, 2022-09-22)
Field: Materials Science — Materials Chemistry

Publisher Correction: Bayesian optimization with experimental failure for high-throughput materials growth Throughput ; Bayesian optimization ; Bayesian probability ; Computer science ; Machine learning ; Artificial intelligence ; Telecommunications Machine Learning in Materials Science
Authors: 浩 舟窪 | HIROSHI FUNAKUBO
Source: Institutional Repositories DataBase (IRDB) (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Experimental discovery of structure-property relationships in ferroelectric materials via active learning Active learning (machine learning) ; Ferroelectricity ; Active perception ; Identification (biology) Ferroelectric and Piezoelectric Materials ; Piezoelectric Actuators and Control ; Dielectric materials and actuators
Authors: Edmund Cheung | Fernando D. León-Cázares | E.I. Galindo-Nava
Source: Materials & Design (article, 2022-02-01)
Field: Materials Science — Metals and Alloys

Optimisation of the hydrogen bake-out treatment in steels via Gaussian processes The presence of hydrogen in structural alloys reduces their ductility, a phenomenon called hydrogen embrittlement. Bake-out heat treatments are employed during processing to allow hydrogen trapped in microstructural features to effuse from the samples, but the optimal times and temperatures depend on the kinetics of hydrogen diffusion in the material. In this work, Gaussian process surrogate models are employed to emulate the outputs of microstructure-sensitive diffusion differential equations in steel. Training the models by sequentially increasing the number of dimensions results in better performances and shorter training times. Two main approaches are developed: single output models with experimental design for the prediction of optimal bake-out times, and multi-output principal component analysis models for the prediction of hydrogen concentration evolution. A novel approach is implemented to shorten the training times of multi-trap models by exploiting the symmetry of the equations with respect to different kinds of traps. The resulting models pave the way for the implementation of Gaussian processes on more computationally expensive diffusion simulations for the optimisation of heat treatments and other applications. Materials science ; Hydrogen ; Hydrogen embrittlement ; Diffusion ; Work (physics) ; Microstructure ; Gaussian ; Statistical physics ; Thermodynamics ; Biological system ; Metallurgy ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep ; Corrosion Behavior and Inhibition
Authors: 浩 舟窪 | HIROSHI FUNAKUBO
Source: Institutional Repositories DataBase (IRDB) (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Experimental discovery of structure-property relationships in ferroelectric materials via active learning: polarization switching and ferroelectric nonlinearities Ferroelectricity ; Polarization (electrochemistry) ; Commutation ; Ferroelectric capacitor ; Hysteresis Ferroelectric and Piezoelectric Materials ; Photorefractive and Nonlinear Optics ; Piezoelectric Actuators and Control
Authors: Yixuan Zhang | Ruiwen Xie | Long, Teng | Günzing, Damian | Wende, Heiko | Ollefs, Katharina J. | Zhang, Hongbin
Source: arXiv (Cornell University) (preprint, 2022-03-15)
Field: Materials Science — Materials Chemistry

Autonomous atomic Hamiltonian construction and active sampling of x-ray absorption spectroscopy by adversarial Bayesian optimization X-ray absorption spectroscopy (XAS) is a well-established method for in-depth characterization of the electronic structure due to its sensitivity to the local coordination and electronic states of the active ions. In practice hundreds of energy points should be sampled during the XAS measurement, most of which are redundant and do not contain important information. In addition, it is also a tedious procedure to estimate reasonable parameters in the atomic Hamiltonian for mechanistic understanding. We implemented an Adversarial Bayesian optimization (ABO) algorithm comprising two coupled BOs to automatically fit the multiplet model Hamiltonian and meanwhile to sample effectively based on active learning. Taking NiO as an example, for simulated spectra which can be well fitted by the atomic model, we found that less than 30 sampling points are enough to obtain the complete XAS with the corresponding crystal field or charge transfer model, which can be selected based on intuitive hypothesis learning. Further application on the experimental spectra, it revealed that less than 80 sampling points can already give reasonable XAS and reliable atomic model parameters. Our ABO algorithm has a great potential for future application in automated physics-driven XAS analysis and active learning sampling. X-ray absorption spectroscopy ; Hamiltonian (control theory) ; Bayesian optimization ; Absorption spectroscopy ; Spectroscopy ; Spectral line ; Electronic structure ; Chemistry ; Sampling (signal processing) ; Computer science ; Algorithm ; Physics ; Mathematics ; Mathematical optimization ; Machine learning ; Quantum mechanics ; Optics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Xinyi Fang | Mengyang Gu | Jianzhong Wu
Source: arXiv (Cornell University) (preprint, 2022-08-13)
Field: Materials Science — Materials Chemistry

Reliable emulation of complex functionals by active learning with error control A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an accurate representation of the nonlinear response surface with a high-dimensional input space. Conventional "space-filling" designs, including random sampling and Latin hypercube sampling, become inefficient as the dimensionality of the input variables increases, and the predictive accuracy of the emulator can degrade substantially for a test input distant from the training input set. To address this fundamental challenge, we develop a reliable emulator for predicting complex functionals by active learning with error control (ALEC). The algorithm is applicable to infinite-dimensional mapping with high-fidelity predictions and a controlled predictive error. The computational efficiency has been demonstrated by emulating the classical density functional theory (cDFT) calculations, a statistical-mechanical method widely used in modeling the equilibrium properties of complex molecular systems. We show that ALEC is much more accurate than conventional emulators based on the Gaussian processes with "space-filling" designs and alternative active learning methods. Besides, it is computationally more efficient than direct cDFT calculations. ALEC can be a reliable building block for emulating expensive functionals owing to its minimal computational cost, controllable predictive error, and fully automatic features. Emulation ; Curse of dimensionality ; Computer science ; Latin hypercube sampling ; Algorithm ; Fidelity ; Model predictive control ; Gaussian ; Gaussian process ; Surrogate model ; Sampling (signal processing) ; Artificial intelligence ; Mathematics ; Machine learning ; Control (management) ; Monte Carlo method Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Gabriel M. Nascimento | Elton Ogoshi | A. Fazzio | Carlos Mera Acosta | Gustavo M. Dalpian
Source: arXiv (Cornell University) (preprint, 2022-01-24)
Field: Materials Science — Materials Chemistry

High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property. Spintronics ; Workflow ; Bayesian optimization ; Computer science ; Density functional theory ; Inverse ; Inference ; Ab initio ; Materials science ; Algorithm ; Computational chemistry ; Artificial intelligence ; Physics ; Mathematics ; Chemistry ; Ferromagnetism ; Database ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Surface and Thin Film Phenomena