jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuri V. Flores | Adam Polak | Jéremié Jambet | David Stothard | Marko Haertelt
Source: IEEE Sensors Journal (article, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Point of Interest Mid-Infrared Spectroscopy for Inline Pharmaceutical Packaging Quality Control Good manufacturing practice for medicinal products is laid down in several guidelines and Directives of the European Commission. Those regulations imply, among other aspects, that medicinal products manufacturers have to ensure that the final products are fit for their intended use and do not place patients at risk due to the inadequate safety, quality or efficacy. For the case of manufacturing of pharmaceutical blisters, the attainment of this quality objective leads often to the resourcing of qualified personnel for final visual verification of the blister pack content. The need for inline content verification of pharmaceutical blisters asks therefore for sensors that provide fast, non-contact and accurate chemical information of each individual blister content. Here we report on a quantum cascade laser (QCL)-based blister-verification sensor. The verification principle is substance chemical identification by means of backscattering mid-infrared spectroscopy. The light source is a palm-size wavelength-tunable mid-infrared QCL with ~1 kHz tuning speed. The blister content verification uses machine vision to obtain the required position information for each individual content and fast spatial scanning facilitated by a 2-axis galvanometer scanner. Diffuse reflectance mid-infrared spectra are acquired at each location and their classification is conducted instantaneously. Different classifier approaches are evaluated and discussed including machine learning and standard cross-correlation to Fourier-transform-infrared (FTIR) data. Altogether, this sensor is capable of scanning a standard 12-pill blister pack in ~0.3 s, whereas this scanning time is essentially related to the desired classification accuracy, but not to the spectral resolution, which is fixed. Using machine learning classification, 100% identification accuracy is demonstrated for 13 different medication-types (i.e., with different chemical nature), whereas only 97.4% identification accuracy is achieved by standard cross-correlation to FTIR data. The used pills have all similar size, shape and color, so that classification by visual inspection is barely possible. Materials science ; Computer science ; Process engineering ; Artificial intelligence ; Environmental science ; Engineering Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Scientific Measurement and Uncertainty Evaluation
Authors: Chiara Panosetti | Yonghyuk Lee | Artem Samtsevych | Christoph Scheurer
Source: The Journal of Chemical Physics (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide The increasing popularity of machine learning (ML) approaches in computational modeling, most prominently ML interatomic potentials, opened possibilities that were unthinkable only a few years ago-structure and dynamics for systems up to many thousands of atoms at an ab initio level of accuracy. Strictly referring to ML interatomic potentials, however, a number of modeling applications are out of reach, specifically those that require explicit electronic structure. Hybrid ("gray box") models based on, e.g., approximate, semi-empirical ab initio electronic structure with the aid of some ML components offer a convenient synthesis that allows us to treat all aspects of a certain physical system on the same footing without targeting a separate ML model for each property. Here, we compare one of these [Density Functional Tight Binding with a Gaussian Process Regression repulsive potential (GPrep-DFTB)] with its fully "black box" counterpart, the Gaussian approximation potential, by evaluating performance in terms of accuracy, extrapolation power, and data efficiency for the metallic Ru and oxide RuO2 systems, given exactly the same training set. The accuracy with respect to the training set or similar chemical motifs turns out to be comparable. GPrep-DFTB is, however, slightly more data efficient. The robustness of GPRep-DFTB in terms of extrapolation power is much less clear-cut for the binary system than for the pristine system, most likely due to imperfections in the electronic parametrization. Ruthenium ; Ruthenium oxide ; Black box ; Materials science ; Gray (unit) ; Computer science ; Chemistry ; Artificial intelligence ; Medicine ; Organic chemistry ; Nuclear medicine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Nuclear Physics and Applications
Authors: A. Nayamadi Mahmoodabadi | M. Modarresi | Mahmood Rezaee Roknabadi | A. Mogulkoc
Source: Computational Materials Science (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Efficient discovery of room temperature magnetic transition metal monolayers assisted by artificial neural network Monolayer ; Transition metal ; Artificial neural network ; Density functional theory ; Antiferromagnetism ; Condensed matter physics ; Ab initio ; Materials science ; Chemistry ; Computer science ; Nanotechnology ; Physics ; Computational chemistry ; Machine learning ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Jia Li | Kun Meng | Xiaohua Yu | Jiongjiong Xu | Yutao Niu | Xiuhan Li | Shan Jin | Ju Rong
Source: Diamond and Related Materials (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Mechanistic insight into a graphene-like stimulus-responsive desalination membrane from molecular dynamics and first principles Desalination ; Nanotechnology ; Graphene ; Membrane ; Adsorption ; Materials science ; Biochemical engineering ; Environmental science ; Chemistry ; Engineering Graphene research and applications ; Membrane Separation Technologies ; Nanopore and Nanochannel Transport Studies
Authors: Sadia Noureen | Iqrar Hussain Syed | Alaa Awad Abdellatif | Muhammad Qasim Mehmood | Yehia Massoud
Source:  (article, 2023-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reducing Complexity and data-set-Size Through Physics Inspired Tandem Neural Network Owing to ample potential and versatile capabilities of artificial intelligence to solve intricate scientific problems, two regression-based artificial neural network (ANN) models are proposed to design and optimize nano-structured meta-atoms. The proposed forward predicting ANN depicts that considering the complete structural and material information of the cylindrical nano-pillar meta-atoms could predict the corresponding electromagnetic (EM) response (amplitude and phase of transmission) with a mean squared error (MSE) as low as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2.1}\times \mathbf{10}^{-\mathbf{3}}$</tex> . Thus, it replaces the conventional EM simulations performed using high-end commercial software's, while significantly saving time and computational resources. Inverse design deep-learning model is also presented, which is connected with the pre-trained forward model and trained in a tandem architecture to provide an optimum set of dimensions and material, given the target response as its input. Furthermore, a comparative study regarding the number of hidden layers of the ANN and the amount of training dataset size is performed for the proposed forward and tandem inverse models to analyze the effect of considering extra underlying physics related information, i.e., wavelength regime and the EM spectral information. This study reveals that considering the extra information can lead to a significant reduction in the obtained MSE. Specifically, the proposed model could achieve a decent MSE even with a smaller amount of training dataset. Hence, the use of artificial intelligence models significantly reduces the training time and computational complexity of the proposed solution. Artificial neural network ; Mean squared error ; Inverse ; Set (abstract data type) ; Computer science ; Reduction (mathematics) ; Artificial intelligence ; Tandem ; Algorithm ; Machine learning ; Mathematics ; Statistics ; Engineering Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: JunHyeok Jang | Jooyong Kim
Source: Polymers (article, 2023-10-18)
Field: Materials Science — Polymers and Plastics

Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and analyzed through experiments. The results revealed that as the length of an individual stitch decreased, the width of the conductive yarn increased. Additionally, variations in the stitch angle resulted in different resistance values in the conductive yarn. These findings provide essential information for optimizing sewing patterns and designing components. Secondly, the comparison between models using multiple linear regression analysis and sewing neural networks was included to show optimized resistance prediction. The multiple linear regression analysis indicated that the stitch length and angle were significant variables affecting the resistance of the conductive thread. The artificial neural network model results can be valuable for optimizing sewing patterns and controlling resistance in various applications that utilize conductive thread. In addition, understanding the resistance variation in conductive thread according to sewing patterns and using optimized models to enhance component performance provides opportunities for innovation and progress. This research is necessary for the textile industry and materials engineering fields and holds high potential for practical applications in industrial settings. Thread (computing) ; Electrical conductor ; Yarn ; Artificial neural network ; Materials science ; Linear regression ; Electrically conductive ; Computer science ; Mechanical engineering ; Biological system ; Composite material ; Artificial intelligence ; Engineering ; Machine learning Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Color perception and design
Authors: Mohsen Soori
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2023-04-17)
Field: Materials Science — General Materials Science

Advanced Composite Materials and Structures Composite materials are used to produce multi-objective structures such as fluid reservoirs, transmission pipes, heat exchangers, pressure vessels due to high strength and stiffness to density ratios and improved corrosion resistance. The mathematical concepts can be used to simulate and analyze the generated mechanical and thermal properties of composite materials regarding to the desired performances in actual working conditions. To solve and obtain the exact solution of the developed nonlinear differential equations in the composite materials, analytical methods can be applied. Mechanical and thermal analysis of complex composite structures can be numerically analyzed using the Finite Element Method (FEM) to increase performances of composite structures in different working conditions. To decrease failure rate and increase performances of composite structures under complex loading system, thermal stress and effects of static and dynamic loads on the designed shapes of composite structures can be analytically investigated. The stresses and deformation of the composite materials under the complex applied loads can be calculated by using the FEM method in order to be used in terms of safety enhancement of composite structures. To increase the safety level as well as performances of the composite structures in different working conditions, crack development in elastic composites can be simulated and analyzed. To develop and optimize the process of composite deigning in terms of mechanical as well as thermal properties under different mechanical and thermal loading conditions, the advanced machine learning systems can be applied. A review in recent development of composite materials and structures is presented in the study and future research works are also suggested. Thus, to increase performances of composite materials and structures under complex loading systems, advanced methodology of composite designing and modification procedures can be provided by reviewing and assessing recent achievements in the published papers. Composite number ; Materials science ; Composite material Material Properties and Applications ; Structural Analysis of Composite Materials
Authors: Marcin Maździarz
Source: Beilstein Journal of Nanotechnology (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Transferability of interatomic potentials for silicene The ability of various interatomic potentials to reproduce the properties of silicene, that is, 2D single-layer silicon, polymorphs was examined. Structural and mechanical properties of flat, low-buckled, trigonal dumbbell, honeycomb dumbbell, and large honeycomb dumbbell silicene phases, were obtained using density functional theory and molecular statics calculations with Tersoff, MEAM, Stillinger-Weber, EDIP, ReaxFF, COMB, and machine-learning-based interatomic potentials. A quantitative systematic comparison and a discussion of the results obtained are reported. Silicene ; Transferability ; Materials science ; Interatomic potential ; Chemical physics ; Graphene ; Nanotechnology ; Molecular dynamics ; Computational chemistry ; Computer science ; Physics ; Chemistry ; Machine learning Graphene research and applications ; Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications
Authors: Xin Shi | Junjie MAN | Weiting YE | Jiangwei Zhu | Li Fu | Yuhong Zheng | Yue Yin | Yan NIU | Xiaojing WANG
Source: Notulae Botanicae Horti Agrobotanici Cluj-Napoca (article, 2023-03-21)
Field: Chemistry — Analytical Chemistry

Lycium species and variety recognition technology based on electrochemical sensing of leaf signals Identification of plant species and variety has important application value in the process of agricultural production. In this work, we try to use electrochemical fingerprinting technology to collect the electrochemical behavior of electrochemically active substances in plant leaf tissues. Twenty Lycium species and varieties were specifically selected to investigate the recognition ability of electrochemical fingerprinting. Two different extraction solvents and electrolytes were used to create different collection environments. The results show that different Lycium spp. can exhibit different electrochemical fingerprints. Different species of the same species exhibit relatively similar electrochemical fingerprints. After the second derivative processing, the electrochemical fingerprint of plants can be used for classification and recognition by different machine learning models. Partial least squares discriminant analysis (PLS-DA), k-nearest neighbor, (KNN), support vector machine (SVM), random forest (RF) and stochastic gradient boosting (SGB) were used to establish recognition model of Lycium spp. The results show that SGB has the best identification accuracy for electrochemical fingerprint after second derivative treatment. Fingerprint (computing) ; Support vector machine ; Random forest ; Pattern recognition (psychology) ; Linear discriminant analysis ; Artificial intelligence ; Lycium ; Identification (biology) ; Partial least squares regression ; Computer science ; Biological system ; Machine learning ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Fatin Abrar Shams | Rashed Hasan Ratul | Ahanf Islam Naf | Syed Shaek Hossain Samir | Samiur Rashid Abir | Fahim Faisal | Mirza Muntasir Nishat | Md. Ashraful Hoque
Source: EAI Endorsed Transactions on AI and Robotics (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

A Machine Learning based Investigative analysis for predicting critical temperature of superconductors INTRODUCTION: Ever since the initial discovery of superconductivity, the fundamental concept and the complex relationship between critical temperature and superconductive materials have been subject to extensive investigation. However, identifying superconductors that exhibit such behavior at normal temperatures remains a significant challenge, and there are still significant gaps in our understanding of this unique phenomenon, particularly regarding the fundamental criteria used to estimate critical temperature.OBJECTIVES: To address this knowledge gap, a plethora of machine learning techniques have been developed to model critical temperatures, given the inherent difficulty in predicting them using traditional methods.METHODS: Additionally, the limitations of the standard empirical formula in determining the temperature range require the development of more advanced and viable methods. This article presents an investigative analysis on the performance achieved by different supervised machine learning algorithms when used with three different feature selection techniques.RESULTS: The stacking model used in this work is found to be the best performer among all the algorithms tested, as reflected by the Root Mean Squared Error (RMSE) of 9.68, R2 score of 0.922, Mean Absolute Error (MAE) score of 5.383, and Mean Absolute Percentage Error (MAPE) score of 4.575.CONCLUSION: Therefore, it is observed that ML algorithms can contribute significantly in the domain of predictive analysis of modeling critical temperatures in superconductors and can assist in developing a robust computer-aided system to aid the education personals and research scientists to further assess the performance of the ML models. Mean squared error ; Machine learning ; Artificial intelligence ; Computer science ; Applicability domain ; Mean absolute percentage error ; Domain (mathematical analysis) ; Range (aeronautics) ; Statistics ; Mathematics ; Artificial neural network ; Materials science Machine Learning in Materials Science
Authors: Simon Ward | Tengfei Cao | Xiang Zhou | Catie Chang | Sharon M. Weiss
Source: Biosensors (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Protein Identification and Quantification Using Porous Silicon Arrays, Optical Measurements, and Machine Learning We report a versatile platform based on an array of porous silicon (PSi) thin films that can identify analytes based on their physical and chemical properties without the use of specific capture agents. The ability of this system to reproducibly classify, quantify, and discriminate three proteins separately is demonstrated by probing the reflectance of PSi array elements with a unique combination of pore size and buffer pH, and by analyzing the optical signals using machine learning. Protein identification and discrimination are reported over a concentration range of two orders of magnitude. This work represents a significant first step towards a low-cost, simple, versatile, and robust sensor platform that is able to detect biomolecules without the added expense and limitations of using capture agents. Analyte ; Biomolecule ; Porous silicon ; Silicon ; Materials science ; Porosity ; Identification (biology) ; Biological system ; Nanotechnology ; Computer science ; Chemistry ; Optoelectronics ; Chromatography Silicon Nanostructures and Photoluminescence ; Advanced biosensing and bioanalysis techniques ; Analytical Chemistry and Sensors
Authors: Anandvinod Dalmiya | Manaf Sheyyab | Eric Mayhew | Kenneth Brezinsky | Patrick T. Lynch
Source:  (article, 2023-06-26)
Field: Chemistry — Analytical Chemistry

Comparison of Infrared Spectroscopic Methods in Predicting DCN of Jet Fuels and Their Blends Using Chemometric Tools Abstract Fuels are made up of thousands of chemical components, but these can be grouped into far fewer chemical functional groups. Fuel properties can be correlated to simpler fuel representations, potentially as simple as the composition of their functional groups. Machine learning (ML) models have been built to predict fuel properties from spectroscopic measurements (preferably in liquid phase). Derived cetane number (DCN) is one such property, which is an indicator of ignition quality in compression-ignition engines. Attenuated total reflectance (ATR) spectroscopy has advantages over transmission-based approaches to overcome challenges associated with the narrow pathlength liquid cells (&amp;lt; 10μm) needed to avoid saturation in strong absorption bands in the mid-infrared. In our previous work, we demonstrated the use of Fourier transform infrared (FTIR) spectra (through transmission) in predicting the DCN of jet fuels after eliminating saturated regions. Convolutional neural network (CNN) models performed the best with a mean percentage error of 3% with an R2 score of 0.931 over a spectral region that includes the fingerprint region. Although those models have shown high predictive capability, since they ignored information from regions likely to show saturated features, it is intuitive that prediction capabilities could be improved further with access to the information in these regions. To evaluate the impact of these saturation regions on DCN estimation, we compare the prediction accuracies and important regions corresponding to DCN obtained from models trained on FT-ATR spectra, which include the regions excluded in the previous work. In this work, uncorrected ATR spectral measurements were used in developing calibration models for predicting DCN of jet fuels and their blends. ATR spectra of jet fuels like F-24, an alcohol-to-jet fuel, Jet-A, etc., and neat hydrocarbons and their mixtures, which in their assembly, represent the chemical functional groups present in jet fuels, were collected using an analytical grade FTIR spectrometer. Spectral measurements between 4000–650cm−1 were used in this analysis, and important spectral regions associated to functional groups correlating to DCN were also identified. Cetane number ; Jet fuel ; Biological system ; Ignition system ; Convolutional neural network ; Chemometrics ; Materials science ; Computer science ; Infrared spectroscopy ; Saturation (graph theory) ; Infrared ; Spectroscopy ; Fourier transform ; Artificial intelligence ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Mathematics ; Machine learning ; Chromatography ; Physics ; Thermodynamics Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: H. Mohit | Sanjay Mavinkere Rangappa | Rapeeporn Srisuk | Suchart Siengchin | Raed H. Althomali | Khalid A. Alzahrani | Abdullah M. Asiri | Anish Khan
Source: Materials Chemistry and Physics (article, 2023-08-31)
Field: Materials Science — Polymers and Plastics

Effect of MWCNT/ Al2O3/ boron nitride fillers based natural/carbon/ innegra fabrics/ SS-WM/ Iron-WM reinforced UV resistant polyester composites Materials science ; Composite material ; Ultimate tensile strength ; Carbon nanotube ; Boron nitride ; Polyester ; Basalt fiber ; Fiber Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Electrospun Nanofibers in Biomedical Applications