jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lenin Javier | S. V. Udaya Kumar Shetty | AK Das | K N Chethan | Laxmikant G. Keni | Sampath Suranjan Salins
Source: Istrazivanja i projektovanja za privredu (article, 2024-01-01)
Field: Materials Science — Biomaterials

Optimization of electrospinning parameters using an artificial neural network (ANN) model for enhanced nanofiber production Electrospinning is a simple and cost-effective technique for creating nanofibers with diverse applications. Optimizing electrospinning parameters is crucial for producing nanofibers with desirable attributes, such as uniform diameter and bead-free morphology. Conventional trial-and-error strategies are frequently protracted and may not necessarily result in optimal outcomes. This investigation delineates the formulation of an artificial neural network (ANN) model specifically designed to systematically optimize electrospinning parameters. Crucial input variables, such as applied voltage, feed rate, and polymer concentration, were utilized to train the ANN model, which was constructed with multiple hidden layers to effectively encapsulate the intricate relationships between input parameters and the resultant nanofiber properties. In this research, an ANN was devised with a 4-3-1 architecture that was trained on a dataset extrapolated from experimental data documented in prior literature and employed the Levenberg-Marquardt algorithm to ascertain robust performance. Upon validation, the model proficiently predicted optimal parameters conducive to the production of smooth, bead-free nanofibers. The model achieved a root mean square error (RMSE) of 7.77%, which is lower than previous models for predicting electrospun Kefiran nanofiber diameter. The results indicate that the ANN-based methodology substantially augments the efficiency and precision of electrospinning parameter optimization, thereby providing a significant resource for researchers and engineers engaged in the domain of nanomaterials. Future investigations could delve into the application of this model to various polymer systems and further refine the ANN architecture to accommodate more intricate electrospinning configurations. Electrospinning ; Nanofiber ; Artificial neural network ; Materials science ; Production (economics) ; Computer science ; Process engineering ; Artificial intelligence ; Composite material ; Engineering ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: P. Suran
Source: Horticultural Science (review, 2024-09-29)
Field: Chemistry — Analytical Chemistry

Use of near-infrared spectrometry in temperate fruit: A review Near-Infrared (NIR) spectrometry has emerged as a promising tool for the non-destructive and rapid analysis of temperate fruit quality, maturity, and other parameters. The technique provides a wealth of information, including details of chemical composition, without damaging the fruit, making it a highly viable alternative to traditional methods. This paper reviews the recent research and applications of NIR spectrometry for fruit evaluation, highlighting its strengths and potential limitations. The analysis shows a significant potential for NIR spectrometry, especially when combined with machine learning and artificial intelligence to handle complex data and improve predictive models. The development of portable NIR spectrometers allows for in-situ quality assessment, expanding its applicability to various fields including on-site quality control. Despite the benefits, this review identifies key challenges including spectral complexity, fruit variability, and the influence of the external environment. Recommendations for future research include focusing on improving calibration and validation of models, increasing predictive accuracy, and developing user-friendly instruments. In addition, standardization of measurement procedures and analytical methods is needed to ensure comparability and reproducibility of results. Further research is needed to fully realize the full potential of NIR spectrometry in fruit quality control. Temperate climate ; Mass spectrometry ; Infrared ; Environmental science ; Chemistry ; Botany ; Biology ; Physics ; Astronomy ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ryosuke Maekawa | H. Nakayoshi | Makoto Kawano | Chika Takai
Source: Advanced Powder Technology (article, 2024-10-31)
Field: Materials Science — Polymers and Plastics

Viscosity control of alumina dispersed resin through design of surface modifier by a QSPR-method Viscosity ; Materials science ; Chemical engineering ; Composite material ; Engineering Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes ; Polymer crystallization and properties
Authors: Gustavo R. Pérez-Lemus | Yinan Xu | Yezhi Jin | Pablo F. Zubieta Rico | Juan Pablo
Source: The Journal of Chemical Physics (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

The importance of sampling the dynamical modes: Reevaluating benchmarks for invariant and equivariant features of machine learning potentials for simulation of free energy landscapes Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computational efficiency. However, questions remain regarding the stability of simulations using these potentials, as well as the extent to which the learned potential energy function can be extrapolated safely. Past studies have encountered challenges when MLIPs are applied to classical benchmark systems. In this work, we show that some of these challenges are related to the characteristics of the training datasets, particularly the inefficient exploration of the dynamical modes and the inclusion of rigid constraints. We demonstrate that long stability in simulations with MLIPs can be achieved by generating unconstrained datasets using unbiased classical simulations, provided that the important dynamical modes are correctly sampled. In addition, we emphasize that in order to achieve precise energy predictions, it is important to resort to enhanced sampling techniques for dataset generation, and we demonstrate that safe extrapolation of MLIPs depends on judicious choices related to the system's underlying free energy landscape and the symmetry features embedded within the machine learning models. Extrapolation ; Computer science ; Stability (learning theory) ; Benchmark (surveying) ; Dynamical systems theory ; Invariant (physics) ; Statistical physics ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: T.T. Dele‐Afolabi | Dong Won Jung | Masoud Ahmadipour | Azmah Hanim Mohamed Ariff | Adeleke Abdulrahman Oyekanmi | M. Kandasamy | Prem Gunnasegaran
Source: Journal of Materials Research and Technology (article, 2024-10-30)
Field: Materials Science — Ceramics and Composites

Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina Chemical attack is one of the most significant issues affecting porous ceramic systems employed as membranes for separation technologies, which necessitate frequent system reliability testing. In this work, the non-linear predictive power of a hybridized machine learning prediction model, specifically Jaya-XGBoost to predict the corrosion-induced mass loss of monolithic and nickel-reinforced porous alumina ceramics has been examined. This study demonstrates the mass loss of monolithic and Ni-reinforced porous alumina developed using rice husk and sugarcane bagasse in acidic and alkaline corrosive media. Based on empirical findings, the formation of a very stable Ni 3 Al 2 SiO 8 spinelloid phase in the RH-graded composites increased their chemical stability in the corrosive environments compared to their monolithic and corresponding SCB-graded counterparts. Corrosion testing data of these specimens were collected and fitted into both XGBoost and Jaya-XGBoost machine learning algorithms. The results showed that the Jaya-XGBoost model performed better in predicting the corrosion-induced mass loss of both the monolithic and the nickel-reinforced porous alumina than the regular XGBoost model in terms of statistical accuracy measures. The Jaya-XGBoost model developed in this study effectively predicted the mass loss in NaOH (R 2 = 0.9984; MAE = 0.0168) and mass loss in H 2 SO 4 (R 2 = 0.9824; MAE = 0.0217) of the monolithic and nickel-reinforced porous alumina. The precision that can be obtained by modifying hyper-parameters with the Jaya method, combined with the well-known accuracy of XGBoost, renders the proposed model novel. Materials science ; Boosting (machine learning) ; Corrosion ; Porosity ; Gradient boosting ; Composite material ; Artificial intelligence ; Computer science Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Eesha Andharia | Hind Alqurashi | Abhiyan Pandit | Bothina Hamad
Source: AIP Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Self-consistent phonon calculations and quartic anharmonic lattice thermal conductivity in 2D InS monolayer Low lattice thermal conductivity (κl) is a crucial factor for higher figure-of merit and hence the efficiency of thermoelectric generators. There are several reports on intrinsically low κl values in two-dimensional (2D) van der Waals materials using density functional theory and molecular dynamics simulations. In general, phonon dispersions are studied at absolute zero temperature using the finite-displacement approach within harmonic approximations. In addition, the κlis calculated using the third-order cubic interatomic force constants (IFCs) by solving the Boltzmann transport equation for phonons. In these calculations, we use quartic IFCs to solve self-consistent phonon equations to obtain dynamical dispersion relations at a finite temperature of 300 K using finite-temperature Green’s function for Bosonic systems in 2D indium sulfide (InS) monolayer. The cubic and quartic IFCs are calculated using machine learning algorithms, namely, the ordinary least square fitting and the least absolute shrinkage and selection operator. It was found that there is a lowering of the renormalized anharmonic phonon frequencies in the dispersion relations at 300 K upon the inclusion of quartic IFCs and anharmonic terms in the case of the InS monolayer. Thus, the κlvalue is reduced to 0.6 W/mK as compared to 0.9 W/mK obtained using cubic IFCs. Anharmonicity ; Phonon ; Condensed matter physics ; Quartic function ; Thermal conductivity ; Physics ; Dispersion relation ; Boltzmann equation ; Quantum mechanics ; Mathematics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Tangsiyuan Zhang | Xinyu Cao | Shuming Zhang | Yuhang Chen | Ya Huang | Min Yu | Xiaoyu Han
Source: Additive manufacturing (article, 2024-09-01)
Field: Chemistry — Organic Chemistry

Transformer neural network based real-time process monitoring and direct visualization of top-down vat photopolymerization Photopolymer ; Materials science ; Transformer ; Artificial neural network ; Visualization ; Process (computing) ; Electrical engineering ; Voltage ; Computer science ; Artificial intelligence ; Composite material ; Engineering Photopolymerization techniques and applications ; Digital Media and Visual Art
Authors: S. Merouane
Source: Journal of Aerosol Science (article, 2024-02-16)
Field: Materials Science — Surfaces, Coatings and Films

A new automatic analysis tool for the determination of primary particle size from electron microscopy images: Application of the Cellpose software Software ; Segmentation ; Computer science ; Sample (material) ; Artificial intelligence ; Set (abstract data type) ; Pattern recognition (psychology) ; Physics Electron and X-Ray Spectroscopy Techniques ; Electrostatics and Colloid Interactions ; Electrical and Bioimpedance Tomography
Authors: Hyun‐Seok Lee | Takuji Oda
Source: The Journal of Physical Chemistry C (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy Despite the considerable success of density functional theory (DFT) in a broad class of materials, there are no exchange–correlation functionals or dispersion corrections that can systematically achieve high accuracy in molten salt simulations; for example, the density is often significantly underestimated. This study proposes a method to construct a correction potential that can fill the difference between DFT and experiments, using KCl as a test case. First, a machine learning interatomic potential (MLIP) with DFT accuracy was constructed. Subsequently, a correction potential was prepared to remove residual stresses brought by the MLIP at experimental densities. It was found that a small cation–anion pairwise correction potential is sufficient to significantly improve not only the density but also other material properties, suppressing calculation errors to a level comparable to the deviation of experimental data. This method is versatile and is expected to help realize experimental accuracy in molten salt simulations. Density functional theory ; Computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Iron and Steelmaking Processes
Authors: Siting Hou | Zhimo Wang | Changjian Xie
Source: The Journal of Physical Chemistry A (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Accurate and Efficient Schemes for Mapping Out Two Isolated Seams of Conical Intersections with Neural Networks Solely Based on Adiabatic Energies: A Case Study of H<sup>+</sup> + NO(<i>X</i><sup>2</sup>Π) → H + NO<sup>+</sup>(<i>X</i><sup>1</sup>Σ<sup>+</sup>) A new scheme in the neural network (NN) diabatization approach that solely utilizes the adiabatic energies for constructing the global diabatic potential energy matrices (PEMs) of the molecular systems with two isolated seams of conical intersections (CIs) is proposed. Taking a prototype charge transfer reaction H<sup>+</sup> + NO(<i>X</i><sup>2</sup>Π) → H + NO<sup>+</sup>(<i>X</i><sup>1</sup>Σ<sup>+</sup>), where two seams of CIs are located at the different linear geometries N-O-H and O-N-H, for example, the diabatization with the new scheme including a diabatic state constraint is shown to map out the topographies of both two linear CIs with 100% of the success rate in 10 different trainings, while the diabatization without such constraint hardly represents CIs, in which the avoided crossings appear instead. Simultaneously, we propose a scheme to separate the whole reactive space into three different regions and define the minimal Euclidean distances for each region to efficiently sample the energy points for the NN trainings. Through adjusting the minimal Euclidean distances, the number of the adiabatic energy data needed in the construction of diabatic PEM can be heavily decreased, lowered from ∼2000 to ∼280 energy points, which is much less than the number (>22 000) of <i>ab initio</i> energy points used in earlier spline diabatic PEM. Further quantum dynamic calculations show that the reaction probabilities, vibrational state distributions, and vibrational state resolved differential cross sections are well reproduced on the new NN diabatic PEM, validating these schemes for constructing the diabatic PEM. Diabatic ; Adiabatic process ; Conical surface ; Conical intersection ; Artificial neural network ; Charge (physics) ; Physics ; Scheme (mathematics) ; Transfer (computing) ; Potential energy ; Energy (signal processing) ; Atomic physics ; Computer science ; Algorithm ; Mathematics ; Quantum mechanics ; Geometry ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Natasha Dropka | Milena Petković | Klaus Böttcher | Martin Holeňa
Source: Journal of Crystal Growth (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Unraveling conditions for W-shaped interface and undercooled melts in Cz-Si growth: A smart approach • CFD modeling of Cz-Si growth was used to generate training data. • Data were generated over a wide range of conditions. • Classification trees assessed growth/furnace parameters for W-shaped interfaces and undercooled Si melts. • Symbolic regression predicted minimal temperature in undercooled Si melts. In Cz-Si growth, concave and W-shaped solid–liquid interfaces and undercooled melts are primary contributors to the degradation of crystal quality, particularly structure loss, defect generation, non-uniform dopant distribution, and crystal twisting, making their avoidance crucial. We employed a classification tree machine learning approach to investigate the importance of 15 process and furnace design parameters and their critical ranges for the formation of various types of W-shaped interfaces and undercooled melts at different scales, both in dimensional and dimensionless forms, and across a wide range of process conditions. Moreover, symbolic regression was used to predict minimal melt temperature based on the aforementioned inputs. Training data were obtained by CFD modeling. The classification tree for combined output identified the Grashof, Reynolds for crystal, and Stefan numbers, along with the percentage of silicon solidified, as the most decisive inputs. Symbolic regression for the temperature of undercooled melt highlighted crucible diameter, pulling rate, and the power of the bottom heater as key parameters. Interface (matter) ; Materials science ; Mineralogy ; Thermodynamics ; Chemistry ; Composite material ; Physics ; Wetting Solidification and crystal growth phenomena ; Silicon and Solar Cell Technologies ; Thin-Film Transistor Technologies
Authors: Mohammad Amin Hariri‐Ardebili
Source: Construction and Building Materials (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Interpretable physics-aware alkali-silica reaction expansion prediction Alkali–silica reaction ; Alkali metal ; Materials science ; Alkali–aggregate reaction ; Composite material ; Forensic engineering ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Koichi Kano | Kenji Koga
Source: Computational Materials Science (article, 2024-07-11)
Field: Materials Science — Materials Chemistry

Deep learning-assisted high-throughput screening of Fe–Co–Ni ternary system using density functional calculations Ternary operation ; Density functional theory ; Throughput ; Materials science ; Chemistry ; Computer science ; Computational chemistry ; Telecommunications Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Christopher Chukwutoo Ihueze | Christian Emeka Okafor | Obende Ezekiel Omeiza
Source: SN Computer Science (article, 2024-08-27)
Field: Materials Science — Polymers and Plastics

Robust Design and Intelligent Modelling of Organic-Based Composites for Armoury Applications Composite material ; Materials science ; Process engineering ; Engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Polymer crystallization and properties
Authors: Khushi Mittal | Kanwarpartap Singh Gill | Mukesh Kumar | Ruchira Rawat
Source:  (article, 2024-05-24)
Field: Chemistry — Analytical Chemistry

Predicting the Red Wine Quality using Exploratory Data Analysis and VGG19 Convolutional Neural Network in Deep Learning The quality of red wine is a multifaceted characteristic influenced by various chemical components. This study adopts a dual approach, integrating Exploratory Data Analysis (EDA) and the VGG19 CNN Model from Deep Learning to anticipate red wine quality. The dataset encompasses diverse chemical features such as acidity, alcohol concentration, volatile acidity, and quality ratings. Utilizing histograms, scatter plots, and correlation matrices, the distribution and interplay of variables are clarified. Subsequently, the robust VGG19 CNN Model is employed to categorize red wines into quality groups. Trained on a subset of the dataset, the model estimates the likelihood of wines falling into distinct quality categories using a binary classification approach. The dataset is divided into training and testing sets for model training and evaluation. Model performance is assessed through metrics like accuracy, precision, recall, and F1-score. Feature selection techniques are applied to identify key chemical properties influencing wine quality. The acquired VGG19 CNN model serves as a valuable interpretive tool, allowing an examination of the relative importance of each factor through coefficient analysis. A comprehensive understanding of chemical constituents is pivotal for enhancing wine production processes, empowering producers to optimize quality and meet customer expectations. Computer science ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Quality (philosophy) ; Wine ; Exploratory analysis ; Data science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Advanced Chemical Sensor Technologies
Authors: Xin Xu | Jun Chen | Shu Liu | Dong H. Zhang
Source: The Journal of Physical Chemistry A (article, 2024-11-20)
Field: Chemistry — Spectroscopy

Differential Cross-Sections for the Vibrationally Excited H + HOD(<i>v</i><sub>OH</sub> = 1–4) → H<sub>2</sub> + OD Reactions Using the time-dependent wave-packet approach, we calculate the first fully converged state-to-state differential cross-sections for the H + HOD(<i>v</i><sub>OH</sub> = 1-4) → H<sub>2</sub> + OD reactions on a highly accurate neural network PES. It is found that, unlike the loss of memory effect observed in the product distributions for low vibrational excitation reactions, high initial OH vibrational excitation significantly influences not only the product vibrational distribution but also the angular distribution. Furthermore, for the H + HOD(<i>v</i><sub>OH</sub> = 3,4) reactions, the total integral cross-sections maintain the pronounced oscillatory structures in the <i>J</i> = 0 probabilities at low collision energies, which originate from the prereactive van der Waals resonances. Notably, the product angular distributions exhibit forward-backward peaked behavior at these energies, akin to those observed in the complex-forming system with deep potential wells. This is attributed to the much longer lifetimes of these resonances compared to those of conventional transition state resonances. Excited state ; Atomic physics ; Physics ; Chemistry Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric chemistry and aerosols