jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ákos Pomázi | Andrea Toldy
Source: Journal of Thermal Analysis and Calorimetry (article, 2022-10-16)
Field: Materials Science — Polymers and Plastics

Predicting the flammability of epoxy resins from their structure and small-scale test results using an artificial neural network model Abstract Developing optimal flame retardant polymer compositions that meet all aspects of a given application is energy and cost-intensive. To reduce the number of measurements, we developed an artificial neural network-based system to predict the flammability of polymers from small-scale test data and structural properties. The system can predict ignition time, peak and total heat release, and mass residue after the burning of reference and flame retarded epoxy resins. Total heat release was predicted most accurately, followed by the peak heat release rate. We ranked the input parameters according to their impact on the output parameters using a sensitivity analysis. This ranking allowed us to establish a relationship between the input and output parameters taking into account their physical content. Flammability ; Artificial neural network ; Epoxy ; Fire retardant ; Ignition system ; Materials science ; Polymer ; Process engineering ; Test data ; Sensitivity (control systems) ; Biological system ; Environmental science ; Composite material ; Computer science ; Thermodynamics ; Engineering ; Machine learning Flame retardant materials and properties ; Fire dynamics and safety research ; Environmental and Industrial Safety
Authors: Hao Li | Musen Zhou | Jessalyn Sebastian | Jianzhong Wu | Mengyang Gu
Source: The Journal of Chemical Physics (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Efficient force field and energy emulation through partition of permutationally equivalent atoms Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of ab initio molecular dynamics simulation. Integrating both atomic force and energy in predictions was found to be more accurate than using energy alone, yet it requires O((NM)<sup>3</sup>) computational operations for computing the likelihood function and making predictions, where N is the number of atoms and M is the number of simulated configurations in the training sample due to the inversion of a large covariance matrix. The high computational cost limits its applications to the simulation of small molecules. The computational challenge of using both gradient information and function values in GPs was recently noticed in machine learning communities, whereas conventional approximation methods may not work well. Here, we introduce a new approach, the atomized force field model, that integrates both force and energy in the emulator with many fewer computational operations. The drastic reduction in computation is achieved by utilizing the naturally sparse covariance structure that satisfies the constraints of the energy conservation and permutation symmetry of atoms. The efficient machine learning algorithm extends the limits of its applications on larger molecules under the same computational budget, with nearly no loss of predictive accuracy. Furthermore, our approach contains an uncertainty assessment of predictions of atomic forces and energies, useful for developing a sequential design over the chemical input space. Force field (fiction) ; Computer science ; Computational complexity theory ; Bottleneck ; Gaussian process ; Kriging ; Computation ; Covariance matrix ; Field (mathematics) ; Algorithm ; Computational model ; Statistical physics ; Computational science ; Gaussian ; Mathematical optimization ; Physics ; Mathematics ; Artificial intelligence ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Software Engineering Research ; Protein Structure and Dynamics
Authors: Sadia Noureen | Sumbel Ijaz | Muhammad Zubair | Muhammad Qasim Mehmood | Yehia Massoud
Source:  (article, 2022-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning enabled modeling tool Meta-Magus for metadevice optimization and design Over the past few years, Deep-learning (DL) based modelling solutions have been presented as an alternate to the timetedious and computationally draining conventional design and optimization procedure of metasurfaces. While designing a phase-based transmission meta-device, such as meta-lenses and meta-holograms etc., the most crucial part is to optimize its unit-cell to ensure maximum electromagnetic (EM) transmission amplitude and full phase coverage (0-2π). Most of the DL-based solutions have resulted in accurate optimization to provide the desired transmission amplitude. But the abrupt discontinuities in the phase response, makes it more challenging to map and predict the optimized structure for full phase coverage. Here, we present a novel DL-based tool named as “Meta-Magus” to design transmission based metaholograms. Meta-Magus consists of two parts: (i) unit-cell optimization, and (ii) phase mask generation. Here, the first component takes target transmission amplitude, phase, material properties, and the wavelength aimed as input, process it via regression based tandem neural network, and provide optimized unit-cell structural parameters as output. Target image whose hologram is to be generated is fed to the second component which comprises of deep convolutional neural network to generate the corresponding phase mask as output. A full-wave commercial simulator then maps the optimized unit-cell onto the generated phase mask and generates the intended meta-hologram. Simulation results of the generated designs exhibit perfect holography, and validates that the model yields excellent predictions of a complete metasurface design from scratch within a matter of seconds. Holography ; Computer science ; Transmission (telecommunications) ; Convolutional neural network ; Phase (matter) ; Amplitude ; Component (thermodynamics) ; Meta learning (computer science) ; Classification of discontinuities ; Artificial neural network ; Algorithm ; Artificial intelligence ; Electronic engineering ; Optics ; Engineering ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Matthew T. Curnan | Dongjae Shin | Wissam A. Saidi | Judith C. Yang | Jeong Woo Han
Source: Acta Materialia (article, 2022-01-14)
Field: Materials Science — Materials Chemistry

Universally characterizing atomistic strain via simulation, statistics, and machine learning: Low-angle grain boundaries Materials science ; Grain boundary ; Dislocation ; Classification of discontinuities ; Thermal diffusivity ; Coupling (piping) ; Statistical physics ; Condensed matter physics ; Thermodynamics ; Composite material ; Microstructure ; Physics Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Hoda Galal | Salah Elsayed | Aida Allam | Mohamed Farouk
Source: Horticulturae (article, 2022-05-14)
Field: Chemistry — Analytical Chemistry

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits. Ripening ; Reflectivity ; Support vector machine ; Artificial intelligence ; Chlorophyll a ; Artificial neural network ; Chlorophyll ; Calibration ; Biological system ; Mathematics ; Environmental science ; Machine learning ; Remote sensing ; Computer science ; Chemistry ; Horticulture ; Statistics ; Botany ; Food science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Banana Cultivation and Research
Authors: Yongxia Wang | Mingyang Fu | Xiaofei Zhang | Dong Jin | Shiyuan Zhu | Yucong Wang | Zhenyu Wu | Jianmin Bao | Xiaogang Cheng | Lei Yang | Linghai Xie
Source: The Journal of Physical Chemistry Letters (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Cubic Nanogrids for Counterbalance Contradiction among Reorganization Energy, Strain Energy, and Wide Bandgap Molecular cross-scale gridization and polygridization of organic π-backbones make it possible to install 0/1/2/3-dimensional organic wide-bandgap semiconductors (OWBGSs) with potentially ZnO-like fascinating multifunctionality such as optoelectronic and piezoelectronic features. However, gridization effects are limited to uncover, because the establishment of gridochemistry still requires a long time, which offers a chance to understand the effects with a theoretical method, together with data statistics and machine learning. Herein, we demonstrate a state-of-the-art 3D cubic nanogridon with a size of ∼2 × 2 × 1.5 nm<sup>3</sup> to examine its multigridization of π-segments on the bandgap, molecular strain energy (MSE), as well as reorganization energy (ROE). A cubic gridon (CG) consists of a four-armed bifluorene skeleton and a thiophene-containing fused arene plane with the Csp<sup>3</sup> spiro-linkage, which can be deinstalled into face-on or edge-on monogrids. As a result, multigridization does not significantly reduce bandgaps (<i>E</i><sub>g</sub> ≥ 4.03 eV), while the MSE increases gradually from 4.72 to 23.83 kcal/mol. Very importantly, the ROE of a CG exhibits an extreme reduction down to ∼28 meV (λ<sup>+</sup>) that is near the thermal fluctuation energy (∼26 meV). Our multigridization results break through the limitation of the basic positively proportional relationship between reorganization energies and bandgaps in organic semiconductors. Furthermore, multigridization makes it possible to keep the ROE small under the condition of a high MSE in OWBGS that will guide the cross-scale design of multifunctional OWBGSs with both inorganics' optoelectronic performance and organics' mechanical flexibility. Band gap ; Semiconductor ; Flexibility (engineering) ; Materials science ; Organic semiconductor ; Enhanced Data Rates for GSM Evolution ; Optoelectronics ; Polymer ; Nanotechnology ; Condensed matter physics ; Physics ; Composite material ; Computer science Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Graphene research and applications
Authors: Ruihao Yuan | Deqing Xue | Jinshan Li | Dezhen Xue | Turab Lookman
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Disentangling the effect of doping chemistry on the energy storage properties of barium titanate ferroelectrics using data science tools Using data science tools including machine learning and statistical analysis, the effects of multiple chemical doping on the energy storage performance of barium titanate based ceramics are investigated from both quantitative and qualitative perspectives. Barium titanate ; Materials science ; Doping ; Energy storage ; Barium ; Ceramic ; Nanotechnology ; Lithium titanate ; Energy (signal processing) ; Computer data storage ; Engineering physics ; Computer science ; Optoelectronics ; Thermodynamics ; Physics ; Composite material ; Metallurgy Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Riley Carriere | Aleksandr Cherniaev
Source: Journal of Aerospace Engineering (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Honeycomb Parameter-Sensitive Predictive Models for Ballistic Limit of Spacecraft Sandwich Panels Subjected to Hypervelocity Impact at Normal Incidence Parameters of the honeycomb core, such as cell size and foil thickness, as well as the material of the core, influence the ballistic performance of honeycomb-core sandwich panels (HCSPs) in the case of hypervelocity impact (HVI) by orbital debris. Two predictive models that account for this influence have been developed in this study: a dedicated ballistic limit equation (BLE) and an artificial neural network (ANN) trained to predict the outcomes of HVI on HCSP. BLE fitting and ANN training was conducted using a database composed of entries resulting from physical and numerical experiments. The new ballistic limit equation was based on the Whipple shield BLE, in which the standoff distance between the facesheets was replaced by a function of the honeycomb cell size, foil thickness, and yield strength of the HC material. The BLE demonstrated excellent accuracy in predicting the ballistic limits of HCSP when tested against a new set of simulation data, with the discrepancy ranging from 1.13% to 5.58% only. The ANN was developed using MATLAB’s version 2018b Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as that employed for the BLE fitting. A comprehensive parametric study was conducted to optimize the ANN architecture, including such parameters as the activation function, the number of hidden layers, and the number of nodes per layer. The resulting ANN demonstrated a very good predictive accuracy when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%. Both of the developed predictive models—the BLE and the ANN—can be recommended for use in the design of spacecraft orbital debris shielding involving honeycomb-core sandwich panels. Ballistic limit ; Hypervelocity ; Parametric statistics ; Honeycomb ; Spacecraft ; Ranging ; Limit (mathematics) ; Space debris ; Function (biology) ; Parametric equation ; Computer science ; Structural engineering ; Mechanics ; Materials science ; Projectile ; Aerospace engineering ; Physics ; Mathematics ; Engineering ; Mathematical analysis ; Geometry ; Statistics ; Composite material High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Nuclear and radioactivity studies
Authors: Ji Zhou | Supat Chupradit | Kirill Ershov | Wanich Suksatan | Haydar Abdulameer Marhoon | May Alashwal | Sami Ghazali | Mohammed Algarni | A.S. El-Shafay
Source: Journal of Molecular Liquids (article, 2022-02-22)
Field: Chemistry — Spectroscopy

Prediction of molecular diffusivity of organic molecules based on group contribution with tree optimization and SVM models Thermal diffusivity ; Support vector machine ; Tree (set theory) ; Computer science ; Diffusion ; Biological system ; Artificial intelligence ; Machine learning ; Mathematics ; Thermodynamics ; Physics Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Electrochemical Analysis and Applications
Authors: Chunyu Guo | Jinzhuo Shi | Ru Feng | Chunyang Zhao | Enhui Wang | Tao Yang | Xinmei Hou
Source: steel research international (article, 2022-11-29)
Field: Materials Science — Ceramics and Composites

Investigation of Multistage Oxidation Behavior of Al<sub>4</sub>SiC<sub>4</sub> Powders with Aid of Back Propagation Artificial Neural Network In order to investigate the multistage oxidation behavior of aluminum silicon carbide (Al 4 SiC 4 ), back propagation artificial neural network (BP‐ANN) has been trained and employed considering the oxidation temperature, time, and aspect ratio. The results denote that the BP‐ANN model can accurately and efficiently simulate the oxidation behavior of Al 4 SiC 4 powders with different reaction laws. In addition, the extrapolation ability suggests that the BP‐ANN model can maintain a high accuracy with the coefficient of determination ≥0.801 to expand the experimental data to 1.2 times the original range. By incorporating a real physical picture model developed by the research group, the experimental data can be further expanded to 2.2 times. This study can provide a new path to recognize the oxidation behavior of materials with multistage oxidation. Extrapolation ; Artificial neural network ; Backpropagation ; Materials science ; Silicon carbide ; Path (computing) ; Biological system ; Carbide ; Aluminium ; Computer science ; Artificial intelligence ; Metallurgy ; Mathematics ; Mathematical analysis Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Semiconductor materials and devices
Authors: Vijay Kumar | Niina Hernández | Michelle Jensen | Rudrajeet Pal
Source: Computers in Industry (article, 2022-09-12)
Field: Materials Science — Polymers and Plastics

Deep learning based system for garment visual degradation prediction for longevity Prolonging garment longevity is a well-recognized key strategy to reduce the overall environmental impact in the textile and clothing sector. In this context, change or degradation in esthetic or visual appeal of a garment with usage is an important factor that largely influence its longevity. Therefore, to engineer the garments for a required lifetime or prolong longevity, there is a need for predictive systems that can forecast the trajectory of visual degradation based on material/structural parameters or use conditions that can guide the practitioners for an optimal design. This paper develops a deep learning based predictive system for washing-induced visual change or degradation of selected garment areas. The study follows a systematic experimental design to generate and capture visual degradation in garment and equivalent fabric samples through 70 cycles in a controlled environment following guideline from relevant washing standards. Further, the generated data is utilized to train conditional Generative Adversarial Network-based deep learning model that learns the degradation pattern and links it to washing cycles and other seam properties. In addition, the predicted results are compared with experimental data using Frechet Inception Distance, to ascertain that the system prediction are visually similar to the experimental data and the prediction quality improves with training process. Context (archaeology) ; Clothing ; Degradation (telecommunications) ; Computer science ; Artificial intelligence ; Process (computing) ; Trajectory ; Deep learning ; Key (lock) ; Machine learning ; Engineering ; Computer security Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Color Science and Applications
Authors: Yiru Wang | Wendi Dong | Wenkai Liang | Bin Yang | Chung K. Law
Source: Proceedings of the Combustion Institute (article, 2022-09-28)
Field: Chemistry — Organic Chemistry

Predicting octane number from species profiles: A deep learning model Computer science ; Convolutional neural network ; Octane ; Calibration ; Multilayer perceptron ; Artificial neural network ; Octane rating ; Combustion ; Autoignition temperature ; Feature (linguistics) ; Ignition system ; Artificial intelligence ; Pattern recognition (psychology) ; Volume (thermodynamics) ; Biological system ; Mathematics ; Chemistry ; Thermodynamics ; Statistics ; Physics Chemical Thermodynamics and Molecular Structure ; Advanced Combustion Engine Technologies ; Spectroscopy and Chemometric Analyses
Authors: Hèlios Sanchis-Alepuz | Monika Stipsitz
Source:  (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks This paper presents a method to simulate the thermal behavior of 3D systems using a graph neural network. The method discussed achieves a significant speed-up with respect to a traditional finite-element simulation. The graph neural network is trained on a diverse dataset of 3D CAD designs and the corresponding finite-element simulations, representative of the different geometries, material properties and losses that appear in the design of electronic systems. We present for the transient thermal behavior of a test system. The accuracy of the network result for one-step predictions is remarkable (0.003% error). After 400 time steps, the accumulated error reaches 0.78 %. The computing time of each time step is 50 ms. Reducing the accumulated error is the current focus of our work. In the future, a tool such as the one we are presenting could provide nearly instantaneous approximations of the thermal behavior of a system that can be used for design optimization. Computer science ; Artificial neural network ; Finite element method ; Graph ; Transient (computer programming) ; Thermal ; Focus (optics) ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Engineering ; Structural engineering Machine Learning in Materials Science ; Heat Transfer and Optimization ; Model Reduction and Neural Networks
Authors: Tatsuya Yokoi | Hirotaka Kato | Yu Oshima | Katsuyuki Matsunaga
Source: Journal of Physics and Chemistry of Solids (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Atomic structures of grain boundaries for Si and Ge: A simulated annealing method with artificial-neural-network interatomic potentials Misorientation ; Interatomic potential ; Simulated annealing ; Molecular dynamics ; Artificial neural network ; Grain boundary ; Annealing (glass) ; Tilt (camera) ; Molecular physics ; Materials science ; Computational physics ; Crystallography ; Chemistry ; Physics ; Computational chemistry ; Microstructure ; Algorithm ; Computer science ; Geometry ; Artificial intelligence Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Semiconductor materials and devices