jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abel Brokkelkamp | Jaco ter Hoeve | Isabel Postmes | Sabrya E. van Heijst | Louis Maduro | Albert V. Davydov | Sergiy Krylyuk | Juan Rojo | Sonia Conesa‐Boj
Source: The Journal of Physical Chemistry A (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric function in 2D materials achieving a spatial resolution down to a few nanometers. This approach is based on machine learning techniques developed in particle physics and makes possible the automated processing and interpretation of spectral images from electron energy loss spectroscopy (EELS). Individual spectra are classified as a function of the thickness with <i>K</i>-means clustering, and then used to train a deep-learning model of the zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS<sub>2</sub> nanoflowers and correlate these electrical properties with the local thickness. Our flexible approach is generalizable to other nanostructured materials and to higher-dimensional spectroscopies and is made available as a new release of the open-source EELSfitter framework. Electron energy loss spectroscopy ; Band gap ; Spectroscopy ; Dielectric ; Materials science ; Electron ; Spectral line ; Dielectric function ; Computational physics ; Resolution (logic) ; Optoelectronics ; Molecular physics ; Optics ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Matthew J. Burn | Paul L. A. Popelier
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

ICHOR: a modern pipeline for producing Gaussian process regression models for atomistic simulations There is now a highly automated electronic tool (called ICHOR) that facilitates the construction and validation of actively learnt machine learning models (Gaussian process regression) for molecules and molecular clusters. Kriging ; Pipeline (software) ; Gaussian process ; Computer science ; Process (computing) ; Regression ; Artificial intelligence ; Machine learning ; Gaussian ; Regression analysis ; Statistical physics ; Mathematics ; Statistics ; Computational chemistry ; Physics ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Yuechen Gao | Yihua Lu | Xi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Mateverse, the Future Materials Science Computation Platform Based on Metaverse Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ultimate goal for many disciplines. In materials science, human intelligence is still irreplaceable from machine learning techniques, where humans can deal with complex correlations in the real world. We design the framework of Mateverse, a materials science computation platform based on Metaverse, which unifies human intelligence, experiment data, and theoretical simulations. In Mateverse, we intensively study the properties of H<sub>2</sub>O, including the liquid and solid phases. We show that we can optimize a new water force field (which we name TIP4P-Meta) directly from the interactions between human and visible properties of H<sub>2</sub>O. This force field is validated to be better than the conventional water model, and new ice polymorphs can be generated. We believe our platform can provide valuable hints in the paradigm upgrade in future computational materials science development. Computer science ; Field (mathematics) ; Data science ; Computation ; Human intelligence ; Upgrade ; Software ; Metaverse ; Artificial intelligence ; Human–computer interaction ; Virtual reality ; Algorithm Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Rongchang Jiang | Jingxin Shen | Xinran Li | Rui Gao | Qinghe Zhao | Zhongbin Su
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Detection and recognition of veterinary drug residues in beef using hyperspectral discrete wavelet transform and deep learning A fast, non-destructive recognition method for veterinary drug residues in beef was proposed to mitigate the laborious sample preparation and long detection times associated with conventional chemical detection techniques. Control beef samples free of veterinary drug residues and four groups of beef sprayed with relevant concentrations of metronidazole, ofloxacin, salbutamol, and dexamethasone under ambient conditions were analyzed by 400-1000 nm hyperspectral imaging followed by multiplicative scatter correction preprocessing. Data dimension reduction was performed using Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), and Discrete Wavelet Transform (DWT) based on Haar, db3, bior1.5, sym5, and rbio1.3 wavelet basis functions. Treated data were subjected to Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) modelling. CNN, MLP, SVM, and RF algorithms achieved overall accuracies of 91.6%, 88.6%, 87.6%, and 86.2%, respectively, when combined with DWT (wavelet basis functions and numbers of transform layers being Haar-4, db3-2, bior1.5-4, and sym5-3, respectively). The algorithm Kappa coefficients (0.89, 0.86, 0.85, and 0.83, respectively) and time consumption for prediction (140.60 ms, 57.85 ms, 70.67 ms, and 87.16 ms, respectively) were also superior to models based on CARS and PCA. DWT combined with deep learning can shorten prediction times, considerably improve the accuracy of classification and recognition, and alleviate the Hughes phenomenon, thus providing a new method for the fast, non-destructive detection and recognition of veterinary drug residues in beef. Keywords: hyperspectral, beef, veterinary drug residues, discrete wavelet transform, convolutional neural network, deep learning DOI: 10.25165/j.ijabe.20221501.6459 Citation: Jiang R C, Shen J X, Li X R, Gao R, Zhao Q H, Su Z B. Detection and recognition of veterinary drug residues in beef using hyperspectral discrete wavelet transform and deep learning. Int J Agric & Biol Eng, 2022; 15(1): 224–232. Discrete wavelet transform ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Hyperspectral imaging ; Support vector machine ; Dimensionality reduction ; Mathematics ; Convolutional neural network ; Haar ; Computer science ; Wavelet ; Wavelet transform Spectroscopy and Chemometric Analyses
Authors: Satish Bukkapatnam
Source: IISE Transactions (review, 2022-06-17)
Field: Materials Science — Materials Chemistry

Autonomous materials discovery and manufacturing (AMDM): A review and perspectives This article presents an overview of the emerging themes in Autonomous Materials Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing interest among the scientists and engineers in the materials and manufacturing domains as well as those in the Artificial Intelligence (AI) and data sciences domains, and it offers immense research potential for the industrial systems engineering (ISE) and manufacturing fields. Although there are a few reviews related to this topic, they had focused exclusively on sequential experimentation techniques, AI/machine learning applications, or materials synthesis processes. In contrast, this review treats AMDM as a cyberphysical system, comprising an intelligent software brain that incorporates various computational models and sequential experimentation strategies, and a hardware body that integrates equipment platforms for materials synthesis with measurement and testing capabilities. This review offers a balanced perspective of the software and the hardware components of an AMDM system, and discusses the current state-of-the-art and the emerging challenges at the nexus of manufacturing/materials sciences and AI/data sciences in this nascent, exciting area. Nexus (standard) ; Field (mathematics) ; Computer science ; Data science ; Software ; Systems engineering ; Engineering ; Manufacturing engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Reza Namakian | Dorel Moldovan | Thomas D. Swinburne
Source: Computational Materials Science (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

Temperature dependent stacking fault free energy profiles and partial dislocation separation in FCC Cu Stacking-fault energy ; Stacking ; Metastability ; Stacking fault ; Materials science ; Dislocation ; Fault (geology) ; Energy (signal processing) ; Anharmonicity ; Condensed matter physics ; Partial dislocations ; Physics ; Composite material ; Geology Microstructure and mechanical properties ; Metallurgy and Material Forming ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ling Xin | Jiwei Hu | Xianliang Wu | Kaiwen Huang | Xianfei Huang
Source: Materials Today Communications (article, 2022-03-31)
Field: Chemistry — Organic Chemistry

Fenton-like degradation of carmine dyes based on artificial intelligence modeling and optimization of reduced graphene oxide loaded iron-cobalt-nickel trimetallic nanocomposites Materials science ; Indigo carmine ; Catalysis ; Graphene ; Cobalt oxide ; Cobalt ; Nickel ; Scanning electron microscope ; Chemical engineering ; Graphite oxide ; Fourier transform infrared spectroscopy ; Hydrogen peroxide ; Nuclear chemistry ; Nanotechnology ; Composite material ; Organic chemistry ; Metallurgy Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Advanced oxidation water treatment
Authors: Mohd Jumain Jalil | Muhamad Azfar Izzat Aziz | Danial Nuruddin Azlan Raofuddin | Intan Suhada Azmi | Mohamad Heiry Mohd Azmi | Mohd Saufi Md Zaini | Izni Mariah Ibrahim
Source: ChemistrySelect (article, 2022-11-16)
Field: Materials Science — Polymers and Plastics

Ring‐Opening of Epoxidized Waste Cooking Oil by Hydroxylation Process: Optimization and Kinetic Modelling Abstract Numerous academics worldwide are interested in utilizing waste as a resource to produce an epoxide. Epoxidation/ring‐opening of oxirane is a common method for producing polyols from vegetable oil. This study examined the effect of the catalyst type and loading on the ring opening of epoxide groups by utilizing performic acid generated in situ. The outcomes demonstrated that the zeolite catalyst at 0.2 g loading produced the highest conversion of oxirane (RCO%). The FTIR data showed that the epoxy ring group‘s location caused the hydroxyl group‘s (O−H) to be observed at 3100–3600 cm −1 in the placement of the epoxy ring group (C−O−C) at 1210–1250 cm −1 . Finally, optimization and kinetic modeling using artificial neural networks, abbreviated as Neural Network, is a model of the human brain that is abstracted, simulated, and trained to obtain the value of R closest to 1. Performic acid ; Epoxide ; Hydroxylation ; Epoxy ; Ring (chemistry) ; Catalysis ; Zeolite ; Fourier transform infrared spectroscopy ; Chemistry ; Epoxidized soybean oil ; Organic chemistry ; Materials science ; Chemical engineering ; Enzyme ; Engineering Polymer composites and self-healing ; Oxidative Organic Chemistry Reactions ; Chemical Synthesis and Reactions
Authors: Song‐Jeng Huang | Matoke Peter Mose | Sathiyalingam Kannaiyan
Source: Materials Today Communications (article, 2022-10-17)
Field: Materials Science — Biomaterials

A study of the mechanical properties of AZ61 magnesium composite after equal channel angular processing in conjunction with machine learning Materials science ; Microstructure ; Grain size ; Magnesium alloy ; Composite material ; Particle size ; Elastic modulus ; Composite number ; Reinforcement ; Magnesium ; Universal testing machine ; Alloy ; Homogeneity (statistics) ; Metallurgy ; Ultimate tensile strength ; Machine learning Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Lan Yin | Yunxin Wu
Source: Materials (article, 2022-06-09)
Field: Materials Science — Biomaterials

Comparison of Constitutive Models and Microstructure Evolution of GW103K Magnesium Alloy during Hot Deformation The characteristics of constitutive behavior and microstructure evolution of GW103K magnesium alloy were investigated via hot compression tests at a strain rate of 0.001-1 s<sup>-1</sup> and a temperature of 623-773 K. The rheological stress of GW103K alloy decreased with increasing temperature or decreasing strain rate during hot deformation. Three models including the Johnson Cook (JC) model, the strain-compensated Arrhenius (SCA) model and back-propagation neural networks (BPNN) were applied to describe the constitutive relationships. Subsequently, the predictability and precision of the models were compared by evaluating the correlation coefficient (R), root mean square errors (RMSE), and relative errors (RE). Compared with the JC and SCA models, the BPNN model was more efficient and had higher prediction accuracy in describing flow stress behavior. Furthermore, EBSD maps confirmed that magnesium alloy easily causes dynamic recrystallization (DRX) during hot deformation. The volume fraction and size of DRX grains increased with decreasing strain rate and/or increasing temperature. Dynamic recrystallization ; Materials science ; Flow stress ; Constitutive equation ; Strain rate ; Microstructure ; Deformation (meteorology) ; Magnesium alloy ; Composite material ; Metallurgy ; Hot working ; Thermodynamics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Murilo Moreira | Matthias Hillenkamp | Giorgio Divitini | Luiz H. G. Tizei | Caterina Ducati | M. A. Cotta | Varlei Rodrigues | D. Ugarte
Source: Microscopy and Microanalysis (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part I, Reducing Reconstruction Bias Scanning transmission electron microscopy is a crucial tool for nanoscience, achieving sub-nanometric spatial resolution in both image and spectroscopic studies. This generates large datasets that cannot be analyzed without computational assistance. The so-called machine learning procedures can exploit redundancies and find hidden correlations. Principal component analysis (PCA) is the most popular approach to denoise data by reducing data dimensionality and extracting meaningful information; however, there are many open questions on the accuracy of reconstructions. We have used experiments and simulations to analyze the effect of PCA on quantitative chemical analysis of binary alloy (AuAg) nanoparticles using energy-dispersive X-ray spectroscopy. Our results demonstrate that it is possible to obtain very good fidelity of chemical composition distribution when the signal-to-noise ratio exceeds a certain minimal level. Accurate denoising derives from a complex interplay between redundancy (data matrix size), counting noise, and noiseless data intensity variance (associated with sample chemical composition dispersion). We have suggested several quantitative bias estimators and noise evaluation procedures to help in the analysis and design of experiments. This work demonstrates the high potential of PCA denoising, but it also highlights the limitations and pitfalls that need to be avoided to minimize artifacts and perform reliable quantification. Principal component analysis ; Dimensionality reduction ; Computer science ; Curse of dimensionality ; Noise reduction ; Estimator ; Pattern recognition (psychology) ; Artificial intelligence ; Noise (video) ; Chemical imaging ; Algorithm ; Mathematics ; Statistics ; Hyperspectral imaging ; Image (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications
Authors: Dashuang Liao | Manting Wang | Ka Fai Chan | Chi Hou Chan | Haogang Wang
Source: IEEE Antennas and Wireless Propagation Letters (article, 2022-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Deep-Learning Enabled Discrete Dielectric Lens Antenna for Terahertz Reconfigurable Holographic Imaging A deep-learning enabled discrete dielectric lens (DDL) antenna with terahertz hologram reconfigurability is proposed. The antenna is constructed by two cascaded discrete dielectric lenses, which are designed based on a diffractive deep neural network (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NN) with an improved loss function, fed by a static horn. The DDL antenna can achieve dynamic holographic imaging by a simple mechanical translation of the perfect electric conductor (PEC) mask attached to the first lens instead of locally controlling individual meta-atoms through incorporating active elements or phase change materials with complicated feeding networks. The phase profiles for the DDL antenna design are obtained by training four customized input field patterns and corresponding anticipated output target images with the modified D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NN. Dynamic switching of four number images “1, 2, 3, 4” is demonstrated by both full-wave simulated and experimental results. The proposed DDL antenna and design strategy present a new approach to achieve wavefront reconfiguration, especially in the absence of tunable components at high frequencies. Terahertz radiation ; Holography ; Dielectric ; Lens (geology) ; Optics ; Antenna (radio) ; Reconfigurable antenna ; Materials science ; Optoelectronics ; Computer science ; Physics ; Dipole antenna ; Telecommunications ; Antenna efficiency Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Terahertz technology and applications
Authors: Michiel Larmuseau | Koenraad Theuwissen | Kurt Lejaeghere | Lode Duprez | Tom Dhaene | Stefaan Cottenier
Source: Scripta Materialia (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Towards accurate processing-structure-property links using deep learning Microstructure ; Materials science ; Deep learning ; Artificial intelligence ; Martensite ; Set (abstract data type) ; Property (philosophy) ; Computer science ; Metallurgy Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Thermography and Photoacoustic Techniques
Authors: Chongjie Gao | Jingjing Qiu | Shiren Wang
Source: Journal of Advanced Manufacturing and Processing (article, 2022-02-16)
Field: Chemistry — Organic Chemistry

In‐situ curing of <scp>3D</scp> printed freestanding thermosets Abstract Direct‐ink‐writing (DIW) provides a high‐efficiency way for thermoset printing while rapid in‐situ curing has a significant role in manufacturing rate, quality, performance, and flexibility. In this review, in‐situ curing methods that can be integrated into DIW were discussed, including frontal polymerization, electromagnetic heating, photochemistry, electron beam, and resistance heating curing. The in‐situ process monitoring and curing kinetic analysis technologies such as differential scanning calorimetry (DSC), Raman spectroscopy, Fourier transform infrared spectroscopy (FT‐IR), broadband dielectric spectroscopy (BDS), ultrasonic dynamic mechanical analysis (UDMA), fluorescence spectroscopy, were briefly presented. The working mechanism and features of these characterization measurements are studied. Furthermore, machine learning and other artificial intelligence tools used for the optimization of printing materials, topology design, printing path, and defect detection sensitivity are reviewed. Finally, some future research directions for the DIW and in‐situ curing of thermosets are addressed. Curing (chemistry) ; Thermosetting polymer ; Materials science ; Differential scanning calorimetry ; Fourier transform infrared spectroscopy ; Epoxy ; Raman spectroscopy ; Polymerization ; Spectroscopy ; Composite material ; Polymer ; Chemical engineering ; Optics ; Engineering Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Epoxy Resin Curing Processes
Authors: Damla Okulu | Fatih Selımefendıgıl | Hakan F. Öztop
Source: Engineering Analysis with Boundary Elements (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Review on nanofluids and machine learning applications for thermoelectric energy conversion in renewable energy systems Nanofluid ; Thermoelectric effect ; Computer science ; Renewable energy ; Energy transformation ; Efficient energy use ; Adaptability ; Energy (signal processing) ; Mechanical engineering ; Process engineering ; Materials science ; Nanotechnology ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Building Energy and Comfort Optimization ; Solar Thermal and Photovoltaic Systems
Authors: Valentina Košmerl | Ivan Štajduhar | Marko Čanađija
Source: Neural Computing and Applications (article, 2022-06-04)
Field: Materials Science — Materials Chemistry

Predicting stress–strain behavior of carbon nanotubes using neural networks Artificial neural network ; Carbon nanotube ; Biological system ; Sensitivity (control systems) ; Multilayer perceptron ; Materials science ; Computer science ; Thermal ; Feature (linguistics) ; Convolutional neural network ; Perceptron ; Stress (linguistics) ; Thermal fluctuations ; Residual ; Statistical physics ; Artificial intelligence ; Algorithm ; Nanotechnology ; Thermodynamics ; Physics ; Electronic engineering Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Fuel Cells and Related Materials
Authors: Yi Wang | Fan Zhang | Mengru Wang | Xiaoling Mou | Shuhui Liu | Zheng Jiang | Wei Liu | Ronghe Lin | Yunjie Ding
Source: Angewandte Chemie International Edition (article, 2022-10-18)
Field: Materials Science — Materials Chemistry

Discerning the Contributions of Gold Species in Butadiene Hydrogenation: From Single Atoms to Nanoparticles Identification of the roles of different active sites is vital for the rational design of catalysts. We present a cutting-edge strategy to discern the contributions of different single-atom gold species and nanoparticles in 1,3-butadiene hydrogenation, through coupling of advanced spectroscopic techniques, electron microscopy-based automated image analyses, and steady-state and kinetic studies. While all the carbon-hosted single gold atoms display negligible initial activity, the in situ-evolved gold nanoparticles are highly active. Full metal-species quantification is realized by combining electron-microscopy-based atom recognition statistics and deep-learning-driven nanoparticle segmentation algorithm, allowing the structure-activity correlations for the hybrid catalysts containing different Au architectures to be established. Surface exposure density of Au nanoparticles, as revealed by electron-microscopy-based statistics, is revealed as a new and reliable activity descriptor. Nanoparticle ; Colloidal gold ; Catalysis ; Atom (system on chip) ; Nanotechnology ; Materials science ; Carbon fibers ; Electron microscope ; Microscopy ; Chemistry ; Computer science ; Physics ; Organic chemistry ; Optics ; Composite material Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion