jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giovanni Pilato | Gianpaolo Vitale | Giorgio Vassallo | Daniele Scirè
Source: Nonlinear Dynamics (article, 2024-07-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural modeling of power nonlinear inductors by the E-$$\alpha $$Net network Abstract In this paper, a model for nonlinear ferrite power inductors based on the $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> Net neural network is proposed. The model is able to reproduce the ferrite power inductors inductor behavior up to saturation, considering the core temperature. The $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> Net neural network was used for its generalization capability considering a hybrid approach encompassing a classical weighted interpolation. The model’s effectiveness was experimentally verified by calculating the current flowing through two inductors in an electric circuit in different operating conditions, and has been compared with the two main models found in literature to show the improvement both in terms of the maximum value of the estimated current and the root mean square error. The modeling procedure can be easily extended to inductors with different sizes and core materials due to the features of the $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> Net network and the hybrid approach to retrieve data. Inductor ; Artificial neural network ; Algorithm ; Nonlinear system ; Ferrite core ; Mean squared error ; Interpolation (computer graphics) ; Topology (electrical circuits) ; Computer science ; Root mean square ; Power (physics) ; Electrical engineering ; Mathematics ; Artificial intelligence ; Engineering ; Voltage ; Physics ; Statistics ; Thermodynamics Magnetic Properties and Applications ; Advanced DC-DC Converters ; Silicon Carbide Semiconductor Technologies
Authors: Yan Zhang | Fei Liu | Xiu Qin Li | Yan Gao | Kang Li | Qinghe Zhang
Source: Scientific Data (article, 2024-08-29)
Field: Chemistry — Spectroscopy

Retention time dataset for heterogeneous molecules in reversed–phase liquid chromatography Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups. Chromatography ; Chemistry ; Hydrophilic interaction chromatography ; Reversed-phase chromatography ; High-performance liquid chromatography ; Phase (matter) ; Retention time ; Organic chemistry Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Chromatography in Natural Products
Authors: Sergei F. Vyboishchikov
Source: Liquids (article, 2024-08-12)
Field: Chemistry — Organic Chemistry

Solvation Enthalpies and Free Energies for Organic Solvents through a Dense Neural Network: A Generalized-Born Approach A dense artificial neural network, ESE-ΔH-DNN, with two hidden layers for calculating both solvation free energies ΔG°solv and enthalpies ΔH°solv for neutral solutes in organic solvents is proposed. The input features are generalized-Born-type monatomic and pair electrostatic terms, the molecular volume, and atomic surface areas of the solute, as well as five easily available properties of the solvent. ESE-ΔH-DNN is quite accurate for ΔG°solv, with an RMSE (root mean square error) below 0.6 kcal/mol and an MAE (mean absolute error) well below 0.4 kcal/mol. It performs particularly well for alkane, aromatic, ester, and ketone solvents. ESE-ΔH-DNN also exhibits a fairly good accuracy for ΔH°solv prediction, with an RMSE below 1 kcal/mol and an MAE of about 0.6 kcal/mol. Solvation ; Artificial neural network ; Thermodynamics ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Organic chemistry ; Computer science ; Artificial intelligence ; Solvent Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Lishi Zhou | Haotian Wen | Inga Kuschnerus | Shery L. Y. Chang
Source: Nanomaterials (article, 2024-07-09)
Field: Materials Science — Surfaces, Coatings and Films

Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds Morphologies of nanoparticles and aggregates play an important role in their properties for a range of applications. In particular, significant synthesis efforts have been directed toward controlling nanoparticle morphology and aggregation behavior in biomedical applications, as their size and shape have a significant impact on cellular uptake. Among several techniques for morphological characterization, transmission electron microscopy (TEM) can provide direct and accurate characterization of nanoparticle/aggregate morphology details. Nevertheless, manually analyzing a large number of TEM images is still a laborious process. Hence, there has been a surge of interest in employing machine learning methods to analyze nanoparticle size and shape. In order to achieve accurate nanoparticle analysis using machine learning methods, reliable and automated nanoparticle segmentation from TEM images is critical, especially when the nanoparticle image contrast is weak and the background is complex. These challenges are particularly pertinent in biomedical applications. In this work, we demonstrate an efficient, robust, and automated nanoparticle image segmentation method suitable for subsequent machine learning analysis. Our method is robust for noisy, low-electron-dose cryo-TEM images and for TEM cell images with complex, strong-contrast background features. Moreover, our method does not require any a priori training datasets, making it efficient and general. The ability to automatically, reliably, and efficiently segment nanoparticle/aggregate images is critical for advancing precise particle/aggregate control in biomedical applications. Nanoparticle ; Computer science ; Segmentation ; Transmission electron microscopy ; Characterization (materials science) ; Nanoparticle tracking analysis ; Electron tomography ; Materials science ; Artificial intelligence ; A priori and a posteriori ; Image segmentation ; Aggregate (composite) ; Nanotechnology ; Contrast (vision) ; Biological system ; Scanning transmission electron microscopy ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Michał Maciusowicz | Grzegorz Psuj
Source: Applied Sciences (article, 2024-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training. Spectrogram ; Materials science ; Short-time Fourier transform ; Acoustics ; Barkhausen effect ; Window (computing) ; Grain size ; Pattern recognition (psychology) ; Speech recognition ; Artificial intelligence ; Computer science ; Metallurgy ; Physics ; Fourier transform Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Tian-Xing Yang | Peng Dou
Source: Materials & Design (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Prediction of formation energy for oxides in ODS steels by machine learning • Magpie descriptors and physical features were introduced to the machine learning models. • An effective light gradient boosting machine model was established to predict the formation energy of oxides. • The phase and proportions of various nano-oxides in six oxide dispersion strengthened steels were determined. • The formation mechanisms of different kinds of oxides were analyzed based on the results of machine learning models. The phase and proportion of nano-oxides in oxide dispersion strengthened (ODS) steels are determined by the formation energy and the content of oxide-forming elements, particularly minor reactive elements, which in turn influence the macroscopic properties of the ODS steels. To study the phase, morphology, and interfaces of oxides in six ODS steels, transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), and high-resolution transmission electron microscopy (HRTEM) techniques were employed. Subsequently, machine learning (ML) methods were used to predict the formation energies of the oxides in the ODS steels. Among six ML models employed, the light gradient boosting machine (LGBM) model shows the highest accuracy in predicting the formation energy of the oxides, with root mean square error (RMSE) values of 0.18 eV/atom and 0.27 eV/atom, mean absolute error (MAE) values of 0.11 eV/atom and 0.18 eV/atom, and coefficient of determination (R 2 ) values of 0.96 and 0.92 for the training and testing sets, respectively. For Magpie descriptors, the most essential feature is “dev_Electronegativity” with an importance score of 444. The method developed in this study can establish an accurate prediction model for the formation energy of oxides. The knowledge obtained in this work guides the future development of high-performance ODS alloys. Materials science ; Energy (signal processing) ; Metallurgy ; Machine learning ; Computer science Fusion materials and technologies ; Nuclear Materials and Properties ; Metallurgical Processes and Thermodynamics
Authors: Yibo Qiao | Weiping Xie | Shunyuan Lou | Qian Jin | Lichao Zeng | Yining Chen | Qi Sun | Cheng Zhuo
Source:  (article, 2024-06-23)
Field: Materials Science — Surfaces, Coatings and Films

Minimizing Labeling, Maximizing Performance: A Novel Approach to Nanoscale Scanning Electron Microscope (SEM) Defect Segmentation In semiconductor manufacturing, pinpointing nanoscale wafer defects is crucial for yield and reliability. Deep learning methods for defect segmentation rely heavily on large, labor-intensive datasets and focus mainly on macroscopic wafer defects, not nanoscale morphology. Our research introduces a hybrid weakly supervised scanning electron microscope (SEM) defect segmentation system with two sub-networks: one for accurate defect localization and image cropping, another for detailed segmentation. Validated on 1,328 SEM image defects from a real facility, our model surpasses existing weakly supervised methods and equals fully supervised models in accuracy, with 10% labeling effort, providing a novel approach for high-precision defect segmentation. Scanning electron microscope ; Nanoscopic scale ; Materials science ; Segmentation ; Nanotechnology ; Electron microscope ; Computer science ; Optoelectronics ; Artificial intelligence ; Optics ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Yusuf Shaidu | William DeSnoo | Alex Smith | Eric Taw | Jeffrey B. Neaton
Source: The Journal of Physical Chemistry Letters (article, 2024-01-24)
Field: Chemistry — Inorganic Chemistry

Entropic Effects on Diamine Dynamics and CO<sub>2</sub> Capture in Diamine-Appended Mg<sub>2</sub>(dopbdc) Metal–Organic Frameworks Recent measurements [Xu, J.; <i>J. Phys. Chem. Lett.</i> 2019, 10 (22), 7044-7049] have reported temperature-dependent rates of detachment of diamine from Mg sites in diamine-appended Mg<sub>2</sub>(dobpdc) [dobpdc<sup>4-</sup> = 4,4'-dihydroxy(1,1'-biphenyl)-3,3'-dicarboxylic] metal-organic frameworks, a process hypothesized to be a precursor for cooperative CO<sub>2</sub> adsorption, leading to step-shaped isotherms or isobars. Here, we compute the rate of diamine exchange in this system for different diamines using metadynamics simulations based on a density functional theory-derived neural network potential. Reanalyzing recent measurements accounting for entropic effects, we find a positive correlation between the previously reported CO<sub>2</sub> adsorption step pressure and the free energy at room temperature and show that the experiments and simulations are in good qualitative and quantitative agreement. The rates obtained here provide new insight into the chemical dynamics of CO<sub>2</sub> adsorption in a class of materials that are promising for carbon capture and a lower bound on the time scale of cooperative adsorption. Diamine ; Adsorption ; Chemistry ; Metal-organic framework ; Metadynamics ; Physical chemistry ; Inorganic chemistry ; Computational chemistry ; Molecular dynamics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Navreet Kaur | Akanksha Sharma | Vishal Sharma
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-01-03)
Field: Chemistry — Analytical Chemistry

Advancing automobile identification and brand discrimination from tyre rubber through Machine learning algorithms for forensic investigations Natural rubber ; Identification (biology) ; Automotive industry ; Algorithm ; Smoothing ; Computer science ; Artificial intelligence ; Engineering ; Forensic engineering ; Machine learning ; Computer security ; Computer vision ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cultural Heritage Materials Analysis
Authors: Puneet Mishra | Michela Albano‐Gaglio | Maria Font‐i‐Furnols
Source: Journal of Chemometrics (article, 2024-04-18)
Field: Chemistry — Analytical Chemistry

A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction Abstract This study demonstrates a new approach to process hyperspectral images where both the contextual spatial information as well as the spectral information are used to predict sample properties. The deep contextual spatial information is extracted using the deep feature extraction from pretrained resnet‐18 deep learning architecture, while the spectral information was readily available as the average pixel values. To fuse the information in a complementary way, a multiblock modeling approach called sequential orthogonalized partial least squares was used. The sequential model guarantees that the information learned is complementary from spatial and spectral domains. The potential of the approach is demonstrated to predict several physical and chemical properties in pork bellies. Hyperspectral imaging ; Fuse (electrical) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Spatial analysis ; Pixel ; Process (computing) ; Fusion ; Deep learning ; Sensor fusion ; Partial least squares regression ; Feature (linguistics) ; Feature extraction ; Mathematics ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mangladeep Bhullar | Zihao Bai | Akinwumi Akinpelu | Yansun Yao
Source: ChemPhysChem (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Phase Transition in Silicon from Machine Learning Informed Metadynamics Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a large-size system. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure. This approach has revealed the transition path and formation of polycrystalline silicon systems under specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics simulations in gaining insights into complex material behaviors in large-sized systems. Metadynamics ; Silicon ; Chemical physics ; Phase transition ; Materials science ; Nanotechnology ; Chemistry ; Statistical physics ; Computer science ; Computational chemistry ; Molecular dynamics ; Condensed matter physics ; Physics ; Optoelectronics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Sujin Bak | Takuji Oda
Source: Corrosion Science (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Generation and correction of machine learning interatomic potential for simulation of liquid metal corrosion with near experimental accuracy: A study for iron corrosion in liquid lead Corrosion ; Materials science ; Metallurgy ; Liquid metal ; Lead (geology) ; Metal Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Jiachen Wang | Ning He | Yanxue Zhang | Yuan Chang | Chunyan Liu | Jiang Xiangmei | Xie Hong-bin | Shaoyun Chen | Jixing Liu | Junfeng Gao | Wenshuai Zhu | Jiaxu Liu
Source: Fuel (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Enhancing the sulfur resistance of Mn/HZSM-5 catalyst for selective catalytic reduction of NOx by coupling the adsorbent NOx ; Catalysis ; Sulfur ; Selective catalytic reduction ; Adsorption ; Reduction (mathematics) ; Chemistry ; Coupling (piping) ; Inorganic chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Metallurgy ; Combustion Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Masaya Tashiro | Kosuke Ide | Kosei Asano | Satoshi Ishii | Yuta Sugiura | Akira Uchiyama | Hiroki Wakatsuchi
Source: NPG Asia Materials (article, 2024-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-enabled multifunctional single-frequency sensors without external power Abstract IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical spaces, digital twins, autonomous driving, smart cities and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or requiring multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems. Materials science ; Optoelectronics ; Power (physics) ; Nanotechnology ; Engineering physics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Paweł Leszczyński | K. Kutorasiński | Marcin Szewczyk | Jarosław Pawłowski
Source: IEEE Transactions on Power Electronics (article, 2024-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learned Models for Power Magnetic Material Characteristics We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural autoencoder model in an unsupervised manner. The encoder is trying to predict the material parameters of a theoretical model, which is then used in a decoder part. The decoder, using the predicted parameters, reconstructs the input characteristics. The neural model is trained to capture a synthetically generated set of characteristics that can cover a broad range of material behaviors, leading to a model that can generalize on the underlying physics rather than just optimize the model parameters for a single measurement. After setting up the model, we prove its usefulness in the complex problem of modeling magnetic materials in the frequency and current (out-of-linear range) domains simultaneously, for which we use measured characteristics obtained for frequency up to 10 MHz and H-field up to saturation. Power (physics) ; Computer science ; Electrical engineering ; Electronic engineering ; Engineering ; Physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques