jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. Dhopeshawar | Harshit Bansal | Karen Veroy | Hao Shi | Diletta Giuntini
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Non-Intrusive Model Order Reduction for Sintering Applications We are interested in thermo-mechanical problems arising in the context of a practically relevant manufacturing process called sintering. These models can be defined using a non-linear material model, namely the Skorohod-Olevsky Viscous Sintering (SOVS) constitutive model. This SOVS model is used to predict macroscopic sintering behavior, such as shrinkage and density evolution. Also, it relies on material properties such as temperature-dependent viscosity and surface tension. However, high-fidelity simulations of coupled, macroscopic, thermo-mechanical models are computationally intensive. Furthermore, developing reducedorder models addressing the non-linearities is challenging due to the history dependence and presence of internal variables. Performing parametric studies, optimization, real-time control, or parameter estimation for such problems, thus, becomes infeasible. In order to accelerate sintering simulations for such multi-query scenarios, a surrogate model is vital. Here, we present a non-intrusive reduced-order modelling framework based on proper orthogonal decomposition and Gaussian process regression. Furthermore, we discuss the performance of such a surrogate model using different metrics for the two-parameter Arrhenius-type viscosity function Context (archaeology) ; Surrogate model ; Mathematical optimization ; Model order reduction ; Computer science ; Viscosity ; Sintering ; Parametric statistics ; Gaussian process ; Materials science ; Applied mathematics ; Gaussian ; Algorithm ; Mathematics ; Physics ; Composite material Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced Control Systems Optimization
Authors: Xun Zeng | Chuanlai Liu | Chaoyu Zhao | Jie Dong | Franz Roters | Dikai Guan
Source: arXiv (Cornell University) (preprint, 2023-03-07)
Field: Materials Science — Biomaterials

Three-dimensional study of grain scale tensile twinning activity in Mg: A combination of microstructure characterization and mechanical modeling Tensile twinning is a main deformation mode in hexagonal close packed structure metals, so it is important to comprehensively understand twinning mechanisms which are not fully disclosed using 2D or small volume 3D characterization techniques. A large area 3D electron backscatter diffraction (EBSD) measurement and crystal plasticity modeling were carried out to investigate the tensile twinning behaviors in a Mg alloy. The results showed that tensile twinning activity was underestimated using conventional 2D EBSD scans. When compressed to yield point, the examined twin frequency with 2D was lower than that using 3D EBSD. The effects of Schmid factor (SF) on twinning were investigated. Almost all high Schmid factor (SF&gt;0.35) grains were twinned. A surprising high twin frequency of 82% in middle SF (0.35&gt;=SF&gt;=0.15) grains was observed, which was unexpected since the middle SF grains were believed to be unfavorable for twinning. The twin frequency in low SF (SF&lt;0.15) grains was slightly increased from 2D to 3D EBSD due to the small volume of twins. The shear stress maintained a high level and was homogeneously distributed in high SF grains, facilitating twin nucleation and growth. The shear stress was distributed heterogeneously within the middle SF grains, and twins were nucleated within areas with positive shear stress. The shear stress in low SF grains was not favorable for twinning and twins occurred in the vicinity of stress accumulation. Twinning activities in the same grain varied on different layers. It was attributed to the stress fluctuation derived from grain environment changes. Crystal twinning ; Electron backscatter diffraction ; Materials science ; Critical resolved shear stress ; Microstructure ; Nucleation ; Ultimate tensile strength ; Shear (geology) ; Composite material ; Metallurgy ; Chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Edilberto Strauss | Manoel Villas Bôas Júnior | Carla Alves Marinho
Source: Projectus (article, 2023-06-18)
Field: Materials Science — Metals and Alloys

USO DE APRENDIZADO DE MÁQUINA PARA ANÁLISE DE SINAIS TERAHERTZ NA CARACTERIZAÇÃO DE REPAROS COMPÓSITOS DE TUBULAÇÕES OFFSHORE Reparos compósitos de engenharia representam uma importante forma de reabilitação da integridade estrutural de regiões com perda de espessura, também sendo capazes de promover estanqueidade em defeitos tipo furo passante, em decorrência do processo de corrosão. Esta técnica de reparo também confere proteção anticorrosiva externa e apresenta a importante vantagem de ser depositada a frio, o que impulsionou sua aplicação de forma crescente em sistemas offshore. Tais reparos são dimensionados para cada caso de uso e têm uma vida útil projetada, após a qual, devem ser repostos por outros. No entanto, operando sob as severas condições ambientais offshore, aliadas às condições de operação das tubulações, o material compósito que constitui o reparo pode sofrer alterações microestruturais que alterem suas propriedades mecânicas e, consequentemente, sua vida residual em serviço. Visando analisar o efeito da névoa salina sobre tais materiais, amostras de dois fabricantes representativos foram submetidas a diferentes tratamentos de envelhecimento, com variação de temperatura e tempo, e posteriormente avaliadas através de ensaios não destrutivos. Por seu caráter de não contato, facilidade de inspecionar não metálicos e capacidade de caracterizar materiais submetidos a pequenas alterações estruturais, as ondas terahertz foram adotadas como uma metodologia de estudo neste projeto. Como abordagem inicial, após o pré-tratamento e padronização dos dados, os mesmos foram reduzidos em sua dimensionalidade via PCA (Principal Component Analysis) e submetidos a uma classificação por outros métodos de Aprendizado de Máquina. Os resultados mostraram que a técnica de terahertz foi capaz de distinguir com acerto as diferentes amostras em função da temperatura e do tempo de envelhecimento, indicando que a caracterização não destrutiva do reparo compósito no campo é viável e pode responder quanto a permanência do reparo em operação ou não. Os principais benefícios desta abordagem seriam a garantia operacional dos sistemas de tubulação offshore de forma segura e a otimização de recursos com a troca de reparos. Humanities ; Physics ; Philosophy Hydrogen embrittlement and corrosion behaviors in metals
Authors: Reut Kedem Orange | Nadav Opatovski | Dafei Xiao | Boris Ferdman | Onit Alalouf | S.K. Pal | Ziyun Wang | Henrik von der Emde | Michael Weber | Steffen J. Sahl | Aleks Ponjavic | Ady Arie | Stefan W. Hell | Yoav Shechtman
Source: arXiv (Cornell University) (preprint, 2023-03-27)
Field: Materials Science — Surfaces, Coatings and Films

Additive manufacturing of solid diffractive optical elements via near index matching Diffractive optical elements (DOEs) have a wide range of applications in optics and photonics, thanks to their capability to perform complex wavefront shaping in a compact form. However, widespread applicability of DOEs is still limited, because existing fabrication methods are cumbersome and expensive. Here, we present a simple and cost-effective fabrication approach for solid, high-performance DOEs. The method is based on conjugating two nearly refractive index-matched solidifiable transparent materials. The index matching allows for extreme scaling up of the elements in the axial dimension, which enables simple fabrication of a template using commercially available 3D printing at tens-of-micrometer resolution. We demonstrated the approach by fabricating and using DOEs serving as microlens arrays, vortex plates, including for highly sensitive applications such as vector beam generation and super-resolution microscopy using MINSTED, and phase-masks for three-dimensional single-molecule localization microscopy. Beyond the advantage of making DOEs widely accessible by drastically simplifying their production, the method also overcomes difficulties faced by existing methods in fabricating highly complex elements, such as high-order vortex plates, and spectrum-encoding phase masks for microscopy. Microlens ; Fabrication ; Optics ; Wavefront ; Photonics ; Refractive index ; Holography ; Materials science ; Computer science ; Lens (geology) ; Nanotechnology ; Physics Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Advanced optical system design
Authors: Tigran Mansuryan | A. Tonello | K. Krupa | A. Desmouliere | G. Ndong Ntoutoume | V. Sol | C. Lefort | M. Zitelli | M. Ferraro | F. Mangini | Y. Sun | Yago Arosa | B. Wetzel | S. Wabnitz | Vincent Couderc
Source: arXiv (Cornell University) (preprint, 2023-03-31)
Field: Chemistry — Spectroscopy

Spatial Division Multiplexing for Multiplex Coherent Anti-Stokes Raman Scattering We demonstrate how a narrowband pump and a broadband spectrum can be spatially multiplexed by selective coupling them in two distinct modes of a few-mode microstructure fiber. The first mode carries most of the input pump energy, and experiences spectral broadening. Whereas the second mode preserves the narrow bandwidth of the remaining part of the pump. Bimodal propagation, with a power unbalance strongly in favor of the fundamental mode, is naturally obtained by maximizing coupling into the fundamental mode of the fiber. At the fiber output, the nearly monochromatic beam and the supercontinuum carried by the two different modes are combined by a microscope objective, and used as a pump and a Stokes wave for self-referenced multiplex coherent anti-Stokes Raman scattering micro-spectroscopy. The spectral resolution, the signal-to-noise-ratio, and the possible amplification of the remaining pump beam are discussed. Optics ; Supercontinuum ; Raman scattering ; Physics ; Monochromatic color ; Coherent anti-Stokes Raman spectroscopy ; Multi-mode optical fiber ; Bandwidth (computing) ; Materials science ; Optical fiber ; Raman spectroscopy ; Photonic-crystal fiber ; Telecommunications ; Computer science Spectroscopy and Laser Applications ; Optical Coherence Tomography Applications ; Advanced Fiber Laser Technologies
Authors: Dinh Van Tuan | Hanan Dery
Source: arXiv (Cornell University) (preprint, 2023-04-18)
Field: Materials Science — Materials Chemistry

Excitons and trions in monolayer semiconductors with correlated electrons We revisit low-temperature optical spectra of transition-metal dichalcogenide monolayers and point to a possible crystallization of electrons (or holes) at low to moderate charge densities. To calculate the excitonic spectra under such conditions, we introduce the recursion method and compute how the charge density affects the energies, linewidths, and oscillator strengths of exciton and trion complexes. Equally important, we study how excitons and trions in the monolayer evolve when the charge particles gradually transition to a periodic Wigner lattice. The results provide valuable information on the ability to detect whether the particles are ordered through the exciton spectrum. Finally, we calculate the change in exciton energy in cases that the added charge particles have similar and dissimilar quantum numbers (spin and valley) to those of the electron or hole in the exciton. The results of this work shed new light on important optical properties of monolayer semiconductors. Trion ; Exciton ; Monolayer ; Electron ; Condensed matter physics ; Semiconductor ; Oscillator strength ; Physics ; Biexciton ; Spin (aerodynamics) ; Spectral line ; Materials science ; Quantum mechanics ; Nanotechnology 2D Materials and Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Yuehaw Khoo | Sounak Paul | Nir Sharon
Source: arXiv (Cornell University) (preprint, 2023-04-28)
Field: Materials Science — Surfaces, Coatings and Films

Deep Neural-network Prior for Orbit Recovery from Method of Moments Orbit recovery problems are a class of problems that often arise in practice and various forms. In these problems, we aim to estimate an unknown function after being distorted by a group action and observed via a known operator. Typically, the observations are contaminated with a non-trivial level of noise. Two particular orbit recovery problems of interest in this paper are multireference alignment and single-particle cryo-EM modelling. In order to suppress the noise, we suggest using the method of moments approach for both problems while introducing deep neural network priors. In particular, our neural networks should output the signals and the distribution of group elements, with moments being the input. In the multireference alignment case, we demonstrate the advantage of using the NN to accelerate the convergence for the reconstruction of signals from the moments. Finally, we use our method to reconstruct simulated and biological volumes in the cryo-EM setting. Prior probability ; Orbit (dynamics) ; Computer science ; Artificial neural network ; Convergence (economics) ; Operator (biology) ; Noise (video) ; Algorithm ; Deep neural networks ; Mathematical optimization ; Applied mathematics ; Artificial intelligence ; Mathematics ; Aerospace engineering ; Bayesian probability Electron and X-Ray Spectroscopy Techniques ; Geomagnetism and Paleomagnetism Studies ; Nuclear Physics and Applications
Authors: Yiting Wei | Weizhi Guo | Bingo Wing‐Kuen Ling | Yuheng Dai | Qing Liu
Source: Research Square (preprint, 2023-01-24)
Field: Chemistry — Analytical Chemistry

Fusion of multi-regression models based on the histogram information for blood glucose estimation Abstract Diabetes is a chronic disease that severely degrades the human health. Hence, the blood glucose estimation plays an important role for monitoring the diabetic condition. In order to better estimate the blood glucose values, the multi-regression models are employed. It is worth noting that increasing the total number of the regression models would decrease the regression error. Therefore, this paper proposes a method for fusing the various regression models together based on the histogram information of the blood glucose values in the training set. The computer numerical simulation results show that the regression error yielded by our proposed method is significantly lower than those yielded by the existing methods. Also, our proposed method is also applicable for other regression applications. Regression ; Regression analysis ; Histogram ; Statistics ; Computer science ; Estimation ; Artificial intelligence ; Linear regression ; Pattern recognition (psychology) ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Artificial Intelligence in Healthcare
Authors: Zina-Sabrina Duma | Jouni Susiluoto | Otto Lamminpää | Tuomas Sihvonen | Сату-Пиа Рейникайнен | Heikki Haario
Source: arXiv (Cornell University) (preprint, 2023-12-11)
Field: Chemistry — Analytical Chemistry

KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows Partial Least-Squares (PLS) Regression is a widely used tool in chemometrics for performing multivariate regression. PLS is a bi-linear method that has a limited capacity of modelling non-linear relations between the predictor variables and the response. Kernel PLS (K-PLS) has been introduced for modelling non-linear predictor-response relations. In K-PLS, the input data is mapped via a kernel function to a Reproducing Kernel Hilbert space (RKH), where the dependencies between the response and the input matrix are assumed to be linear. K-PLS is performed in the RKH space between the kernel matrix and the dependent variable. Most available studies use fixed kernel parameters. Only a few studies have been conducted on optimizing the kernel parameters for K-PLS. In this article, we propose a methodology for the kernel function optimization based on Kernel Flows (KF), a technique developed for Gaussian process regression (GPR). The results are illustrated with four case studies. The case studies represent both numerical examples and real data used in classification and regression tasks. K-PLS optimized with KF, called KF-PLS in this study, is shown to yield good results in all illustrated scenarios. The paper presents cross-validation studies and hyperparameter analysis of the KF methodology when applied to K-PLS. Partial least squares regression ; Kernel (algebra) ; Mathematics ; Gaussian function ; Radial basis function kernel ; Kernel principal component analysis ; Kernel embedding of distributions ; Variable kernel density estimation ; Principal component regression ; Kernel smoother ; Kernel method ; Chemometrics ; Kernel regression ; Artificial intelligence ; Statistics ; Linear regression ; Pattern recognition (psychology) ; Computer science ; Gaussian ; Regression ; Machine learning ; Support vector machine ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Sadaf Khan | Zhengyuan Shi | Min Li | Qiang Xu
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

DeepSeq: Deep Sequential Circuit Learning Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet effective representation can help to solve multiple downstream EDA tasks by fine-tuning it on a small set of task-related data. However, existing solutions only target combinational circuits, significantly limiting their applications. In this work, we propose DeepSeq, a novel representation learning framework for sequential netlists. Specifically, we introduce a dedicated graph neural network (GNN) with a customized propagation scheme to exploit the temporal correlations between gates in sequential circuits. To ensure effective learning, we propose to use a multi-task training objective with two sets of strongly related supervision: logic probability and transition probability at each node. A novel dual attention aggregation mechanism is introduced to facilitate learning both tasks efficiently. Experimental results on various benchmark circuits show that DeepSeq outperforms other GNN models for sequential circuit learning. We evaluate the generalization capability of DeepSeq on a downstream power estimation task. After fine-tuning, DeepSeq can accurately estimate power across various circuits under different workloads. Computer science ; Benchmark (surveying) ; Task (project management) ; Electronic circuit ; Representation (politics) ; Generalization ; Artificial intelligence ; Combinational logic ; Exploit ; Computer engineering ; Electronic design automation ; Logic gate ; Machine learning ; Algorithm ; Embedded system ; Engineering ; Mathematics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Stefan Kаrtunov
Source: Modern Concepts in Material Science (article, 2023-08-29)
Field: Materials Science — General Materials Science

Change the Properties of Materials. Phase-Related Materials with an Emphasis in Mechatronics A classification of phase-bound materials is made, their properties are determined and methods for obtaining the charge. They are considered to be species technological processes for production of phase-connected materials. Special attention is paid to their application with an emphasis in mechatronics. Emphasis (telecommunications) ; Mechatronics ; Phase (matter) ; Materials science ; Phase change ; Engineering ; Engineering physics ; Chemistry ; Control engineering ; Electrical engineering ; Organic chemistry Material Properties and Applications
Authors: Eman A. Alasadi
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

Investigating the structural and dynamical basis for ion affinity in the ion-binding sites of signaling proteins Lanthanide ; Affinities ; Ionic radius ; Chemistry ; EF hand ; Structural motif ; Biophysics ; Crystallography ; Ion ; Binding site ; Sequence motif ; Stereochemistry ; Peptide sequence ; Biochemistry ; Biology Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Protein Kinase Regulation and GTPase Signaling
Authors: Aritra Chowdhury | Andrea Sottini | Alessandro Borgia | Madeleine B. Borgia | Souradeep Ghosh | Soumik Mitra | Tianjin Yang | Nicola Galvanetto | Rohan Eapen | Paweł Łukijańczuk | Ruijing Zhu | Daniel Nettels | Arindam Kundagrami | Benjamin Schuler
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

Complexes of charged disordered polyelectrolyte-like proteins: From thermodynamics to stuctural plasticity Polyelectrolyte ; Plasticity ; Chemical physics ; Intrinsically disordered proteins ; Thermodynamics ; Materials science ; Chemistry ; Physics ; Statistical physics ; Polymer ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Protein purification and stability ; Protein Structure and Dynamics
Authors: Lujun Zhang | Cai Nuo | Xiaodong Huang | Fan Xinmin | Gao Juanjuan | Gao Jin | Sensen Li | Wang Yan | Chunyan Wang
Source: Research Square (preprint, 2023-10-30)
Field: Chemistry — Analytical Chemistry

Adulteration detection and quantification in olive oil using excitation-emission matrix fluorescence spectroscopy and chemometrics Abstract This study explores the application of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric techniques for the rapid identification and quantification of adulteration in olive oil, particularly in cases where sample quantities are limited. Soybean oil, peanut oil, and linseed oil are introduced into olive oils to simulate various adulterated samples. Our approach involves the application of parallel factor analysis (PARAFAC) for data decomposition, with a following focus on establishing correlations between the decomposed components and the actual adulterated components. This is accomplished through a thorough comparison of the spectral characteristics and score results of the decomposed components, allowing us to attribute them to the actual adulterated components and thereby ultimately enabling us to quantify the levels of actual adulteration. The results proves that EEMF spectroscopy combined with the proposed analysis methods serves as a powerful tool for the rapid detection and quantification of adulteration in olive oil. We also utilize principal component analysis (PCA) to cluster adulterated samples and identify efficient excitation wavelengths, and conduct a comparative analysis between PCA and PARAFAC methods. This study offers a novel perspective and method for quantitatively analyzing adulterants in olive oil through spectral detection, holding the promise of practical application in real-world detection scenarios. Chemometrics ; Principal component analysis ; Olive oil ; Matrix (chemical analysis) ; Complex matrix ; Fluorescence spectroscopy ; Biological system ; Chemistry ; Chromatography ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Fluorescence ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food