jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rania Baghdadi | Hamza Alibi | Faten Fayala | Xianyi Zeng
Source: Fibers and Polymers (article, 2021-01-19)
Field: Materials Science — Polymers and Plastics

Investigation on Stiffness of Finished Stretch Plain Knitted Fabrics Using Fuzzy Decision Trees and Artificial Neural Networks Stiffness ; Artificial neural network ; Overfitting ; Textile ; Decision tree ; Materials science ; Selection (genetic algorithm) ; Computer science ; Fuzzy logic ; Artificial intelligence ; Composite material Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color perception and design
Authors: Gyoung S. Na | Seunghun Jang | Hyunju Chang
Source: Physical Chemistry Chemical Physics (article, 2021-12-02)
Field: Materials Science — Materials Chemistry

Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states The fundamental goal of machine learning (ML) in physical science is to predict the physical properties of unobserved states. However, an accurate prediction for input data outside of training distributions is a challenging problem in ML due to the nonlinearities in input and target dynamics. For an accurate extrapolation of ML algorithms, we propose a new data-driven method that encodes the nonlinearities of physical systems into input representations. Based on the proposed encoder, a given physical system is described as linear-like functions that are easy to extrapolate. By applying the proposed encoder, the extrapolation errors were significantly reduced by 48.39% and 40.04% in <i>n</i>-body problem and materials property prediction, respectively. Extrapolation ; Encoder ; Physical system ; Encoding (memory) ; Nonlinear system ; Computer science ; Property (philosophy) ; Physical property ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Neural Networks and Applications
Authors: Kazuhiko Komatsu | Masahito Kumagai | Ji Qi | Masayuki Sato | Hiroaki Kobayashi
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

An Externally-Constrained Ising Clustering Method for Material Informatics Due to the recent advancement of data science, such as machine learning and big-data analysis, the approach using data science techniques has attracted attention even to develop new materials, called material informatics. In material informatics, clustering is one of the essential data processing techniques to understand thermophysical properties. Thus, clustering quality is a high priority to be considered. To improve clustering accuracy, this paper evaluates Ising-based clustering methods using an annealing machine. As an annealing machine minimizes the energy of an Ising model, the Ising-based clustering methods define the clustering as an Ising model to minimize the sum of intra-cluster distances among data. Since the non-Ising-based clustering methods conventionally used in materials informatics perform pseudo-optimization, the Ising-based clustering methods can achieve high clustering accuracy. The experimental results show that the Ising-based clustering method with externally-defined constraint achieves higher clustering accuracy with an affordable execution time than the conventional K-means clustering method. Cluster analysis ; Ising model ; Computer science ; Correlation clustering ; CURE data clustering algorithm ; Data mining ; Consensus clustering ; Data stream clustering ; Simulated annealing ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Joanna Krajewska-Śpiewak | Jan Turek | Józef Gawlik
Source: Management and Production Engineering Review (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Maintenance Supervision of the Dies Condition and Technological Quality of Forged Products in Industrial Conditions Wear of the working surfaces of the forging dies in the process of manufacturing products with the die forging technique leads to deterioration of their operational properties as well as their technological quality. A characteristic feature of production in small and medium-sized enterprises is the high variability of the product range and short production series, which can be repeated in the case of re-orders by customers. In this type of production conditions, a technological criterion in form of – a change in the characteristic and selected dimension of forging is usually used to assess the quality of products. An important problem is, whether by taking up another order for a series of the same type of product, it will be possible to implement it with the existing die, or should a new die be made? As a result of the research carried out in the company implementing this type of contract, a procedure was proposed for forecasting the abrasive wear of die working surfaces on the basis of a technological criterion, easy to determine in the conditions of small and medium-sized enterprises. The paper presents the results of the wear assessment of a die made out of hot-work tool steel X37CrMoV5-1 (WCL) and dies made of 42CrMo4 alloy structural steel with hardfacing working surfaces by F-818 wire. To determine and forecast the process of die wear, a mathematical model in the form of neural networks was used. Their task was to forecast the ratio of the increment in introduced wear intensity indicator to the number of forgings made during the process. Taking into account Forging ; Die (integrated circuit) ; Quality (philosophy) ; Manufacturing engineering ; Process (computing) ; Production (economics) ; Abrasive ; Product (mathematics) ; Mechanical engineering ; Computer science ; Engineering ; Mathematics Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Engineering and Environmental Studies
Authors: Haoxiang Lin | Shuqian Ye | Xi Zhu
Source: Carbon (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

Geometry Orbital of Deep Learning (GOODLE): A uniform carbon potential Atomic orbital ; Bilayer graphene ; Geometry ; Graphene ; Carbon fibers ; Materials science ; Molecular orbital ; Lattice (music) ; Chemical physics ; Molecular physics ; Nanotechnology ; Physics ; Molecule ; Quantum mechanics ; Composite material ; Mathematics ; Electron Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Stylianos Tsopanidis | Shmuel Osovski
Source: Materials & Design (article, 2021-12-07)
Field: Materials Science — Materials Chemistry

A graph-based workflow for extracting grain-scale toughness from meso-scale experiments We introduce a novel computational framework designed to allow an experimentalist to extract the local toughness associated with crack growth along an individual grain boundary from fracture experiments on standard specimens. The proposed framework relies on the ability of a graph neural network to perform high accuracy predictions of the micro-scale material toughness, utilizing a limited size dataset that can be obtained from standard fracture experiments. We analyze the functionality of the different components of the framework, focusing on the minimum size of the dataset required for the network training following an initial training cycle on a synthetic dataset. We demonstrate the high efficiency of the algorithm in accurately predicting the local (>68%) and global (>95%) crack growth toughness in scenarios where the toughness dependence on the microstructural parameters is substantially different than the one used for the synthetic dataset. The merit of the proposed framework arises from its flexibility: it is not limited to a specific mathematical description of the toughness at the micro-scale and simplicity: the required experiments are rather standard. As such, the proposed framework enables the study of a wide range of material microstructure properties and the investigation of their influence on the material toughness. Toughness ; Materials science ; Fracture toughness ; Workflow ; Graph ; Scale (ratio) ; Microstructure ; Flexibility (engineering) ; Computer science ; Composite material ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Rock Mechanics and Modeling ; Fatigue and fracture mechanics
Authors: J Akhir | A Rindang | P C Ayu
Source: IOP Conference Series Earth and Environmental Science (article, 2021-06-01)
Field: Chemistry — Analytical Chemistry

Caffeine content calibration model on green beans arabica Mandailing Natal coffee using NIRS and artificial neural network Abstract Generally, Near Infrared Reflectance (NIR) spectroscopy is a non-destructive method that can identify the chemical content of agricultural products. In this study, several pretreatment data were used to reduce some noises from original NIR spectra, namely normalization (0-1) and Multiple Scatter Correction (MSC) and calibration model of caffeine content using Artificial Neural Network (ANN). Result from wavelength analysis showed that the caffeine content of Mandailing Natal green bean arabica coffee was in the wavelengths of 1,208.9 nm and 1,728.91 nm. A good ANN architecture result was obtained with normalization/MSC which was 13-3-1 at 1,000 iterations, which was indicated by r = 0.9845, CV = 4.80% and │RMSEC-RMSEP│= 0.0383. Normalization (sociology) ; Caffeine ; Calibration ; Near-infrared spectroscopy ; Artificial neural network ; Materials science ; Mathematics ; Optics ; Artificial intelligence ; Biology ; Computer science ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Coffee research and impacts ; Advanced Chemical Sensor Technologies
Authors: Song Wang | Zili Wu | Sheng Dai | De‐en Jiang
Source: Angewandte Chemie (article, 2021-03-04)
Field: Materials Science — Materials Chemistry

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters Abstract Although the coordinates of the metal atoms can be accurately determined by X‐ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep‐learning approach that can accelerate determination of hydride locations in single‐crystal X‐ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3− and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry. Nanoclusters ; Hydride ; Neutron diffraction ; Nanochemistry ; Metal ; Crystallography ; Materials science ; Crystal structure ; Physical chemistry ; Inorganic chemistry ; Chemistry ; Nanotechnology ; Metallurgy Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Harald Martens
Source: NIR news (article, 2021-11-25)
Field: Chemistry — Analytical Chemistry

Interpretable machine learning with an eye for the physics: Hyperspectral Vis/NIR “video” of drying wood analyzed by hybrid subspace modeling Chemometric multivariate analysis based on low-dimensional linear and bilinear data modelling is presented as a fast and interpretable alternative to more fancy “AI” for practical use of Big Data streams from hyperspectral “video” cameras. The purpose of the present illustration is to find, quantify and understand the various known and unknown factors affecting the process of drying moist wood. It involves an “interpretable machine learning” that analyses more than 350 million absorbance spectra, requiring 418 GB of data storage, without the use of black box operations. The 159-channel high-resolution hyperspectral wood “video” in the 500–1005 nm range was reduced to five known and four unknown variation components of physical and chemical nature, each with its spectral, spatial and temporal parameters quantified. Together, this 9-dimensional linear model explained more than 99.98% of the total input variance. Hyperspectral imaging ; Subspace topology ; Chemometrics ; Bilinear interpolation ; Artificial intelligence ; Computer science ; Range (aeronautics) ; Pattern recognition (psychology) ; RGB color model ; Biological system ; Machine learning ; Computer vision ; Materials science Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Cultural Heritage Materials Analysis
Authors: Wei Ding | Jian Chen | Ximing Li | Xi Xiang | Kang-Ping Ye | Huabing Wu | Digang Fan | Rui‐Xin Wu
Source:  (article, 2021-05-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning assisted heat-resistant metamaterial absorber design Metamaterial absorbers (MMA) have great potential in civilian and military applications because of their large degrees of design freedom to achieve broadband absorption with thin thickness. In modern applications, great-performance absorbers require both broad-band absorption with low thickness and highly heat-resistant characteristics. In this paper, a deep-learning-based composite-network (DLCN) is proposed to efficiently design the MMA working at S to X bands with 10-mm-thick thermal barrier layer. The trained DLCN can accurately predict the reflection coefficients of the meta-atoms that constitute the MMA by their configuration parameters, including geometric and material parameters. Using the evolutionary algorithm (EA) to search the parameter space with the mapping function given by DLCN, we obtain the optimal configuration parameters of the meta-atoms and their filling ratio in the MMA that satisfies the demands best. The design method using EA and the DLCN is highly efficient and can be extended to design other multi-functional metamaterials. Metamaterial ; Broadband ; Metamaterial absorber ; Materials science ; Absorption (acoustics) ; Reflection (computer programming) ; Computer science ; Function (biology) ; Optoelectronics ; Composite number ; Optimal design ; Tunable metamaterials ; Composite material ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Thomas Arnold | Martin De Biasio | Raghavendra Kammari | Krithika Sayar-Chand
Source:  (article, 2021-04-08)
Field: Chemistry — Analytical Chemistry

Development of VIS/NIR hyperspectral imaging system for industrial sorting applications Hyperspectral imaging systems are used in industrial sorting solutions to differentiate materials by subtle spectral features (spectral fingerprint) which are not resolvable by the human eye. Prominent applications are the detection of contaminants in food or the separation of different types of plastic. Hyperspectral line scan systems are suitable for many high throughput applications. A line across the sample, perpendicular to the direction of the relative movement, is projected into an imaging spectrograph. The spectral information for each pixel along this line is projected along the second axis of the two-dimensional detector chip. By spatial scanning of the sample the spectral data cube gets recorded. This paper presents the development of a laboratory hyperspectral imaging system capable of acquiring spectral data in the range from 400nm to 1700 nm. Therefore, Specim hyperspectral cameras FX10 and FX17 are used. The laboratory setup mimics an industrial sorting machine and will be used for application development. State of the art machine learning algorithms are used for data classification. Selected sorting applications are presented. Hyperspectral imaging ; Full spectral imaging ; Sorting ; Data cube ; Computer science ; Artificial intelligence ; Computer vision ; Spectral imaging ; Sample (material) ; Pixel ; Detector ; Remote sensing ; Spectrograph ; Spectral signature ; Pattern recognition (psychology) ; Geology ; Data mining ; Physics ; Spectral line ; Algorithm Spectroscopy and Chemometric Analyses
Authors: Peter R. Griffiths | Tequila A. L. Harris
Source: Journal of Coatings Technology and Research (article, 2021-08-06)
Field: Materials Science — Materials Chemistry

Machine learning workflow for microparticle composite thin-film process–structure linkages Microparticle ; Materials science ; Composite number ; Process (computing) ; Workflow ; Composite material ; Process engineering ; Chemical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Natalie Boehnke | Joelle P. Straehla | Hannah C. Safford | Mustafa Kocak | Matthew G. Rees | Melissa M. Ronan | Danny Rosenberg | Charles H. Adelmann | Raghu R. Chivukula | Namita Nabar | Adam G. Berger | Nicholas G. Lamson | Jaime H. Cheah | Hojun Li | Jennifer A. Roth | Angela N. Koehler | Paula T. Hammond
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-06)
Field: Materials Science — Biomaterials

Massively parallel pooled screening reveals genomic determinants of nanoparticle-cell interactions Abstract To accelerate the translation of cancer nanomedicine, we hypothesize that integrated genomic screens will improve understanding of the cellular processes governing nanoparticle trafficking. We developed a massively parallel high-throughput screening method leveraging barcoded, pooled cancer cell lines annotated with multi-omic data to investigate cell association patterns across a nanoparticle library spanning a range of formulations with clinical potential. This approach identified both the materials properties and cell-intrinsic features mediating nanoparticle-cell association. Coupling the data with machine learning algorithms, we constructed genomic nanoparticle trafficking networks and identified nanoparticle-specific biomarkers, including gene expression of SLC46A3. We engineered cell lines to validate SLC46A3 as a biomarker whose expression inversely predicts liposomal nanoparticle uptake both in vitro and in vivo. We further demonstrated the predictive capabilities extend beyond liposomal nanoparticles, regulating both uptake and transfection efficacy of solid lipid nanoparticles. Our work establishes the power of massively parallel pooled cell screens for nanoparticle delivery and enables the identification and utilization of biomarkers to rationally design nanoformulations for specific patient populations. Nanomedicine ; Massively parallel ; Computational biology ; Nanoparticle ; Nanotechnology ; Transfection ; High-throughput screening ; Cell ; Biomarker discovery ; Computer science ; Chemistry ; Cell culture ; Materials science ; Biology ; Bioinformatics ; Proteomics ; Gene ; Genetics ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Hilal Kıvrak | Sarwar Khalid | Aykut Çağlar | Duygu Alpaslan | Kadir Selçuk | Tuba Erşen Dudu | Nahit Aktaş
Source: Energy Storage (article, 2021-09-25)
Field: Materials Science — Polymers and Plastics

The preparation, characterization, and electrooxidation of colloidal palladium modified poly(methacrylic acid) hydrogel as formic acid fuel cell anode catalyst Abstract At present, poly(methacrylic acid) (MA), poly(MA) hydrogel, and Pd‐doped poly(MA) hydrogel modified electrodes are constructed on graphite pencil (G). The Pd precursor is coated on the poly(MA)/G electrodes surface using the electrodeposition method. These electrodes are characterized by thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy, and scanning electron microscopy‐energy dispersive X‐ray. Poly(MA) hydrogel and Pd‐doped poly(MA) hydrogel are successfully prepared by the polymerization and further electrodeposition. Formic acid electro‐oxidation (FAEO) activities are examined with cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). Poly(MA)/G and Pd doped poly(MA) electrodes exhibit promising electro‐catalytic activity with 60 mA current density, greater than literature values. Pd‐doped poly(MA) hydrogel electrodes are promising electrodes for FAEO. Thermogravimetric analysis ; Materials science ; Cyclic voltammetry ; Chronoamperometry ; Dielectric spectroscopy ; Methacrylic acid ; Palladium ; Formic acid ; Fourier transform infrared spectroscopy ; Electrode ; Catalysis ; Electrochemistry ; Polymerization ; Nuclear chemistry ; Polymer chemistry ; Chemical engineering ; Chemistry ; Organic chemistry ; Polymer ; Composite material Conducting polymers and applications ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: N. V. Krishna Prasad | T. ANIL BABU | M.S.S.R.K.N. Sarma | S. RAMESH | K. Nirisha | Thomas Mathew | N. MADHAVI
Source: Journal of Optoelectronic and Biomedical Materials (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

ROLE OF POROUS NANOMATERIAL’S IN WATER PURIFICATION, ELECTRONICS, DRUG DELIVERY AND STORAGE: A COMPREHENSIVE REVIEW Nanoporous materials and their study gained tremendous significance in view of their potential applications such as drug delivery, water purification, biosensing, electronics and storage etc. Based on their synthesis with required shape and size make them application oriented. Nanoporous materials are materials with pore size of hundred nanometres or less. They have inorganic or organic framework supporting regular porous structure. The pores in these materials are occupied with fluid. These materials represent a transition from atom to solid in which pores of uniform shape and diameter need to be obtained for them to be used in some applications. Nanoporous materials possess specific electric, optical and magnetic properties that make them highly potential in applications related to signal transmission, energy, biological applications, catalysis, gas storage and medicine. In spite of existing nanoporous by nature tailored materials can be produced with combination of polymers with different melting points. A nanoporous material with accurate pore sizes allow certain matter to pass through, while blocking others. In view of their tailored properties they find extensive applications at present and in near future. Keeping this in mind, an attempt was made to review the potential applications of these materials with more emphasis on water treatment and drug delivery reported in the last five years in a nut shell. Nanoporous ; Nanotechnology ; Materials science ; Nanomaterials ; Drug delivery ; Electronics ; Porous medium ; Porosity ; Polymer ; Chemistry ; Composite material Advanced Nanomaterials in Catalysis ; Brain Tumor Detection and Classification ; Metal-Organic Frameworks: Synthesis and Applications