jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tanu Pittie | Kartikeya Kartikeya | Naresh Bhatnagar | NM Anoop Krishnan | Thilak Senthil | Subramaniam Rajan
Source: Journal of Composite Materials (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

Building a predictive soft armor finite element model combining experiments, simulations, and machine learning Despite its relevance for law enforcement applications, the design of soft armor has mainly been based on a trial-and-error approach. In this paper, a combined experimental, machine learning, and finite element analysis framework is used to build a predictive numerical model for the analysis and hence, design of soft armor. The material models for major components of the soft armor certification system—bullet, shoot pack, straps, and clay backing, are first constructed using laboratory tests and publicly available data. Next, three metrics, namely, back face signature (BFS), number of penetrated shoot-pack layers, and mushrooming of the bullet are established to gauge the model’s accuracy with respect to the laboratory ballistic test data. A machine learning (ML) model is used as a surrogate to predict the BFS and the number of eroded elements. Finally, optimized material model parameters are obtained through ML-based surrogate model and Bayesian optimization algorithm. The final validation of the developed framework is carried out using laboratory ballistic test data involving multiple shots on the shoot pack. The results indicate that reliable predictive data can be obtained using the developed process, and likely, can be extended for use in modeling other impact simulations. Armour ; Finite element method ; Computer science ; Test data ; Bayesian optimization ; Surrogate model ; Process (computing) ; Machine learning ; Structural engineering ; Materials science ; Engineering High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Transportation Safety and Impact Analysis
Authors: Guoyang Zhang | Qiang Zhu | Hongcen Zheng | Shujuan Zhang | Jing Ma
Source: Chemosphere (article, 2023-11-09)
Field: Chemistry — Organic Chemistry

Prediction of free radical reactions toward organic pollutants with easily accessible molecular descriptors Reactivity (psychology) ; Chemistry ; Molecular descriptor ; Radical ; Linear regression ; HOMO/LUMO ; Biological system ; Pollutant ; Quantitative structure–activity relationship ; Computational chemistry ; Molecule ; Computer science ; Organic chemistry ; Machine learning ; Stereochemistry Free Radicals and Antioxidants ; Electrochemical Analysis and Applications ; Chemistry and Chemical Engineering
Authors: Yuanchuang Lin | Ruihuan Cheng | Tiangui Liang | Weixiong Wu | Song Li | Wei Li
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Understanding the influence of secondary building units on the thermal conductivity of metal–organic frameworks <i>via</i> high-throughput computational screening The thermal conductivity of metal-organic frameworks (MOFs) has garnered increasing interest due to their potential applications in energy-related fields. However, due to the diversity of building units, understanding the relationship between MOF structures and their thermal conductivity remains an imperative challenge. In this study, we predicted the thermal conductivity (<i>κ</i>) of MOFs using equilibrium molecular dynamics (EMD) simulations and investigated the contribution of structure properties to their thermal conductivity. It is revealed that the arrangement of secondary building units (SBUs) with a closer distance of metal atoms, a larger proportion of metal elements, and transition metal elements (Fe, Mn, and Co) leads to high thermal conductivity. To generally quantify the influence of such factors on thermal conductivity, the pathway factors with SBU influence (<i>P</i><sub>m</sub>) were proposed and can be used to efficiently classify structures into high, medium, and low thermal conductivity types. It was found that <i>P</i><sub>m</sub> indicates that MOFs with <i>met</i> topology tend to have high thermal conductivity, while <i>rna</i> and <i>pcu</i> topologies naturally tend to possess medium and low thermal conductivity. Moreover, it was also suggested that taking <i>P</i><sub>m</sub> as a descriptor in the machine learning algorithms can significantly improve the prediction accuracy for thermal conductivity. This study offers molecular insight into the impact of various SBUs on thermal conductivity in framework-based nanomaterials, which may guide the rational design of nanoporous materials with desirable thermal conductivity. Thermal conductivity ; Throughput ; Metal-organic framework ; Conductivity ; High-throughput screening ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Composite material ; Physical chemistry ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yiyang Tong | Qian Wang | Zhen Wang | Zihui Liang | Qiang Xu | Danying Zuo | Hong Yi
Source: Textile Research Journal (article, 2023-04-05)
Field: Materials Science — Polymers and Plastics

Prediction of parameters in the laser fading process of denim using convolutional neural networks Laser technology has been widely used in ecological fading of denim. However, the setting of parameters in the laser fading process mainly depends on the subjective experience of workers. After many attempts, an approximate effect can hardly be obtained. There are various defects in this way such as low fading efficiency, poor fading effect and high production cost, etc. This study proposed a novel method for predicting the parameters of the denim laser fading process using convolutional neural network technology. It starts from data collection, and uses laser technology to select different combinations of laser parameters to conduct laser fading experiments on 100% cotton indigo denim fabrics, and obtains denim image datasets of different laser fading effects. The prediction model of parameters in the laser fading process of denim was established by learning from the dataset with the convolutional neural network. The model could intelligently generate the approximation parameter group according to the fading image and performed well on the validation dataset with finite prediction error. Based on the obtained parameters, it could be effectively used and dramatically reduced the manpower and material resources in setting up a denim laser fading process. Fading ; Denim ; Convolutional neural network ; Artificial neural network ; Process (computing) ; Computer science ; Fading distribution ; Laser ; Artificial intelligence ; Materials science ; Telecommunications ; Rayleigh fading ; Optics ; Channel (broadcasting) ; Composite material ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Ayman Negm | Mohamed H. Bakr | Matiar M. R. Howlader | Shirook M. Ali
Source: Nanomaterials (article, 2023-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft A reconfigurable metasurface constitutes an important block of future adaptive and smart nanophotonic applications, such as adaptive cooling in spacecraft. In this paper, we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using a convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model material properties as image maps. We avoid the dimensionality mismatch problem using the operating wavelength as an input to the network. As a case study, we model the response of a reconfigurable absorber that employs the phase transition of vanadium dioxide in the mid-infrared spectrum. The feed-forward model is used as a surrogate model and is subsequently employed within a pattern search optimization process to design a passive adaptive cooling surface leveraging the phase transition of vanadium dioxide. The results indicate that our model delivers an accurate prediction of the metasurface response using a relatively small training dataset. The proposed patterned vanadium dioxide metasurface achieved a 28% saving in coating thickness compared to the literature while maintaining reasonable emissivity contrast at 0.43. Moreover, our design approach was able to overcome the non-uniqueness problem by generating multiple patterns that satisfy the design objectives. The proposed adaptive metasurface can potentially serve as a core block for passive spacecraft cooling applications. We also believe that our design approach can be extended to cover a wider range of applications. Spacecraft ; Computer science ; Emissivity ; Block (permutation group theory) ; Materials science ; Electronic engineering ; Optics ; Aerospace engineering ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Polydiacetylene-based materials and applications
Authors: Muhamad Haziq Hasnul Hadi | Pin Jern Ker | Hui Jing Lee | Yang Sing Leong | Vimal Angela Thiviyanathan | M. A. Hannan | M. Z. Jamaludin | Mohd Adzir Mahdi
Source: IEEE Transactions on Automation Science and Engineering (article, 2023-01-25)
Field: Chemistry — Analytical Chemistry

Measuring Color Index of Transformer Oil–Enabling Single-Wavelength Spectroscopy With Artificial Neural Network-Fuzzy Logic Model Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others. Artificial neural network ; Mean squared error ; Transformer ; Fuzzy logic ; Artificial intelligence ; Transformer oil ; Mathematics ; Computer science ; Algorithm ; Statistics ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Nicole M. North | Abigail Enders | Morgan L. Cable | Heather C. Allen
Source: ACS Omega (article, 2023-06-29)
Field: Chemistry — Spectroscopy

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States' National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0-100 <i>m</i>/<i>z</i>. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models' targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data. Mass spectrometry ; Artificial intelligence ; NIST ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network ; Transfer of learning ; Machine learning ; Fragmentation (computing) ; Logistic regression ; Python (programming language) ; Data mining ; Chemistry ; Natural language processing ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Yu Yang | Yunshan Zhao | Lifa Zhang
Source: Applied Physics Letters (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Machine learning boosting the discovery of porous metamaterials with an abnormal thermal transport property Normally, the introduction of porous structures into materials can tune their thermal conductivity, showing great applications in thermal management and thermoelectric energy harvesting. However, the ability of disorder changing the thermal conductivity of porous materials has seldom been explored. In this work, we show that an introduction of disorder into the macroscopic porous materials with a certain porosity can lead to a desired effective thermal conductivity over a large range, where an abnormal enhancement of ∼7.9% and a normal reduction of ∼44% at room temperature are predicted by the machine-learning-optimized algorithm. All of these theoretical calculation results are further verified by our experiments performed in the current work by using the steady-state thermal flux method. Moreover, when these periodic units are artificially connected, a structural anisotropy up to 40 is achieved, which can be further used to adjust the direction of the thermal flux in a well-controlled way. Our work provides an efficient and convenient approach for designing high-performance porous materials with specific thermal conductivity and high structural anisotropy for various applications in thermal management. Thermal conductivity ; Materials science ; Anisotropy ; Porosity ; Porous medium ; Thermoelectric effect ; Thermal ; Work (physics) ; Metamaterial ; Thermoelectric materials ; Heat flux ; Composite material ; Heat transfer ; Mechanical engineering ; Optoelectronics ; Mechanics ; Thermodynamics ; Optics ; Physics ; Engineering Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: О. Е. Narivskyi | С. А. Субботин | Tеtiana Pulina | Serhii Leoshchenko | М. S. Khoma | N. B. Ratska
Source: Materials Science (article, 2023-05-01)
Field: Materials Science — Metals and Alloys

Modeling of Pitting of Heat Exchangers Made of 18/10 Type Steel in Circulating Waters Chloride ; Heat exchanger ; Materials science ; Titanium ; Solid mechanics ; Metallurgy ; Structural material ; Thermodynamics ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Metallurgical Processes and Thermodynamics
Authors: Abderrahim Diane | Taoufiq Saffaj | Bouchaîb Ihssane | Reda Rabie
Source: Heliyon (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples. Hyperspectral imaging ; Partial least squares regression ; Mean squared error ; Feature selection ; Chemometrics ; Boosting (machine learning) ; Artificial intelligence ; Computer science ; Predictive modelling ; Mathematics ; Pattern recognition (psychology) ; Statistics ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Yunhua Lu | Kangli Zeng | Qingwei Zhang | Jun’an Zhang | Lin Cai | Jiangling Tian
Source: Chemical Engineering Science (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

A simple and efficient graph Transformer architecture for molecular properties prediction Computer science ; Transformer ; Theoretical computer science ; Graph ; Algorithm ; Artificial intelligence ; Topology (electrical circuits) ; Mathematics ; Engineering ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Vikram Korede | Frederico Marques Penha | Vincent de Munck | Lotte Stam | T.M.A.R. Dubbelman | Nagaraj Nagalingam | Maheswari Gutta | Pingping Cui | Daniel Irimia | A.E.D.M. van der Heijden | Herman J. M. Kramer | Hüseyin Burak Eral
Source: Crystal Growth & Design (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Design and Validation of a Droplet-based Microfluidic System To Study Non-Photochemical Laser-Induced Nucleation of Potassium Chloride Solutions Non-photochemical laser-induced nucleation (NPLIN) has emerged as a promising primary nucleation control technique offering spatiotemporal control over crystallization with potential for polymorph control. So far, NPLIN was mostly investigated in milliliter vials, through laborious manual counting of the crystallized vials by visual inspection. Microfluidics represents an alternative to acquiring automated and statistically reliable data. Thus we designed a droplet-based microfluidic platform capable of identifying the droplets with crystals emerging upon Nd:YAG laser irradiation using the deep learning method. In our experiments, we used supersaturated solutions of KCl in water, and the effect of laser intensity, wavelength (1064, 532, and 355 nm), solution supersaturation (<i>S</i>), solution filtration, and intentional doping with nanoparticles on the nucleation probability is quantified and compared to control cooling crystallization experiments. Ability of dielectric polarization and the nanoparticle heating mechanisms proposed for NPLIN to explain the acquired results is tested. Solutions with lower supersaturation (<i>S</i> = 1.05) exhibit significantly higher NPLIN probabilities than those in the control experiments for all laser wavelengths above a threshold intensity (50 MW/cm<sup>2</sup>). At higher supersaturation studied (<i>S</i> = 1.10), irradiation was already effective at lower laser intensities (10 MW/cm<sup>2</sup>). No significant wavelength effect was observed besides irradiation with 355 nm light at higher laser intensities (≥50 MW/cm<sup>2</sup>). Solution filtration and intentional doping experiments showed that nanoimpurities might play a significant role in explaining NPLIN phenomena. Supersaturation ; Nucleation ; Crystallization ; Laser ; Irradiation ; Wavelength ; Laser power scaling ; Microfluidics ; Filtration (mathematics) ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Optoelectronics ; Nanotechnology ; Optics ; Chromatography Crystallization and Solubility Studies ; Spectroscopy and Quantum Chemical Studies ; nanoparticles nucleation surface interactions