jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuzhou Hao | Junwei Che | Xiaoying Wang | Xuejie Li | Turab Lookman | Jun Sun | Xiangdong Ding | Zhibin Gao
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

Copper delocalization leads to ultralow thermal conductivity in chalcohalide CuBiSeCl2 Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various applications. Among these, CuBiSeCl_2 has recently been experimentally identified with remarkably low lattice thermal conductivity (k_L). In this study, we employ Wigner transport theory combined with neuroevolution machine learning potential (NEP)-assisted self-consistent phonon calculations to unravel the microscopic origins of this low k_L. Our findings reveal that the delocalization and weak bonding of copper atoms are key contributors to the strong phonon anharmonicity and wavelike tunneling (random walk diffusons). These insights deepen our understanding of the relationship between bonding characteristics, anharmonicity, delocalization, and vibrational dynamics, paving the way for the design and optimization of CuBiSeCl_2 and analogous materials for advanced phonon engineering applications. Thermal conductivity ; Copper ; Delocalized electron ; Conductivity ; Materials science ; Thermal ; Optoelectronics ; Condensed matter physics ; Business ; Physics ; Metallurgy ; Composite material ; Meteorology ; Quantum mechanics Solid-state spectroscopy and crystallography ; Crystal Structures and Properties ; Phase-change materials and chalcogenides
Authors: Anuj Kumar | Shveta Mahajan
Source:  (article, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Integrating NIR Hyperspectral Techniques with Neural Network for Barley Seeds Classification Nondestructive identification of seed varieties from different origins is crucial in optimizing crop improvement, plant growth, and advancement in breeding methods. Evaluating varietal purity is essential for seed quality. Traditional barley seed classification relies on visual inspection, which is a subjective and error-prone process and is time-consuming. Moreover, chemical testing involves the breaking of seeds which highlights the necessity for exploring non-destructive methods. The integration of Near-infrared (NIR) spectroscopy with neural networks offers a promising alternative. In this study, we have used a hyperspectral imaging dataset consisting of 34 different Indian barley varieties (1008 per variety). Six spectral pre-processing techniques were used to extract and pre-treat the mean reflectance spectrum associated with each seed, namely Savitzky-Golay (SG) smoothing, SG first derivative, SG second derivative, detrending, standard normal variate (SNV), and multiplicative scatter correction (MSC). Further, we have used five different classifiers: K-nearest neighbors (KNN), partial least squares discrimination analysis (PLS-DA), artificial neural networks (ANN), support vector machine techniques (SVM), and convolutional neural networks (CNN). CNN demonstrated superior classification accuracy when implementing it on unprocessed data, whereas the ANN model outperformed it when combined based on the SG2 pre-processing method. Their respective classification accuracy rates were $98.44 \%$ and $98.88 \%$. The current study examined the use of an ANN model coupled with the NIR-HSI technology to accurately, quickly, and nondestructively classify different types of barley seeds. Hyperspectral imaging ; Computer science ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: I Wayan Budiastra | Sutrisno MARJAN | Nissa Adiarifia | Inna Novianty | Yunisa Tri Suci
Source: INMATEH Agricultural Engineering (article, 2024-08-06)
Field: Chemistry — Analytical Chemistry

NON-DESTRUCTIVE PREDICTION OF OIL AND FREE FATTY ACID OF OIL PALM FRUITLETS USING NEAR-INFRARED SPECTROSCOPY AND HYBRID CALIBRATION METHOD The study used near-infrared reflectance spectroscopy and hybrid calibration methods to predict oil and free fatty acid content of oil palm fruitlets non-destructively. The reflectance and chemical content of oil palm fruitlets were measured and the calibration between near infrared spectra and chemical content was performed using hybrid calibration methods. The developed hybrid calibration model was validated to predict oil and free fatty acid of other oil palm fruitlets. The best models to predict oil and free fatty acid of oil palm fruitlets respectively were the hybrid model of Partial Least Square-Artificial Neural Network with 25 factor components (R2 = 0.96; SEP= 2.21%, RPD = 4.79) and 19 factor components (R2 = 0.96; SEP= 0.25%, RPD = 4.24) using Savitzky-Golay first derivative spectra pre-treatment. Palm oil ; Calibration ; Environmental science ; Near-infrared spectroscopy ; Fatty acid ; Infrared spectroscopy ; Materials science ; Chemistry ; Pulp and paper industry ; Mathematics ; Optics ; Organic chemistry ; Engineering ; Agroforestry ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Dmitriy Glyzin | С. Д. Глызин | Andrey Kolesov
Source: Izvestiya VUZ Applied Nonlinear Dynamics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A new approach to mathematical modeling of chemical synapses The purpose of this work is to study a new mathematical model of a ring neural network with unidirectional chemical connections, which is a singularly perturbed system of differential-difference equations with delay. Methods. A combination of analytical and numerical methods is used to study the existence and stability of special periodic solutions in this system, the so-called traveling waves. Results. The proposed methods make it possible to show that the ring system under study allows the number of stable traveling waves to increase with the number of oscillators in the network. Conclusion. In this article, we rethink and refine the previously proposed method of mathematical modeling of chemical synapses. On the one hand, it was possible to fully take into account the requirement of the Volterra structure of the corresponding equations and, on the other hand, the hypothesis of saturating conductivity. This makes it possible to observe the principle of uniformity: the new mathematical model is based on the same principles as the previously proposed model of electrical synapses. Computer science ; Cognitive science ; Psychology thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Jun Chen | Tan Jin | Zhe‐Ning Chen | Chong Liu | Wei Zhuang
Source: Artificial Intelligence Chemistry (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Adsorption kinetics of H2O on graphene surface based on a new potential energy surface The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about -1000 cm−1 and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics. Graphene ; Physisorption ; Adsorption ; Molecular dynamics ; Kinetic energy ; Chemical physics ; Potential of mean force ; Materials science ; Diffusion ; Potential energy ; Kinetics ; Thermodynamics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Thermal properties of materials
Authors: Matías E. di Mauro | Benoı̂t Braı̈da | Ion Errea | Trinidad Novoa | Julia Contreras‐García
Source: arXiv (Cornell University) (preprint, 2024-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecularity: a fast and efficient criterion for probing superconductivity We present an efficient criterion for probing the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of superconducting DFT. We first apply this descriptor to a model system, the hydrogen chain, which allows to prove two main concepts: i) that the electron localization changes very little when the transition from the normal to the superconducting state takes place, i.e. that it can be described at the DFT level from the normal state; and ii) that the formation of molecules can be characterized within this theoretical framework, enabling to filter out systems with marked molecular character and hence with low potential to be good superconductors. These two ideas, are then exploited in real binary and ternary systems, showing i) that the bonding type can be characterized automatically; and ii) that this provides a new index which enables to feed machine learning algorithms for a better prediction of critical temperatures. Overall, this sets a grounded theoretical scenario for an automatic and efficient high-throughput of potential hydrogen based superconductors. Superconductivity ; Physics ; Theoretical physics ; Condensed matter physics Organic and Molecular Conductors Research ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Tyler C. Salrin | Caio Barca Bragatto | Collin J. Wilkinson
Source: Frontiers in Materials (article, 2024-05-10)
Field: Chemistry — Electrochemistry

Investigating the role of network former interactions on charge carrier diffusivity in glasses Ionic transport is a critical property for the glass industry, since emerging applications such as sensors, batteries, and electric melting are based on the phenomenon. Short-range interactions (anion-charge carrier) have not been able to explain the total activation barrier observed experimentally, and, as such, it is critical to understand the larger role of all ions in a glass, not just the carrier and the ‘site’ ions. This research focuses on the role of network formers and their impact on diffusion in glasses, something that current models lack an explicit explanation of. Atomistic simulations with randomly generated parameters for the cation potentials and classical simulations were used to determine the diffusion coefficients and activation energies for synthetic network formers. Using this database, explainable machine learning algorithms were employed to explore network former interactions and determine which parameters are the most influential for ion diffusion. Results suggest that the bond length of the cations changes the geometry of the structure contributing the greatest to cation-modifier interactions. Thermal diffusivity ; Charge (physics) ; Materials science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Thermodynamics Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Shibo Liu | Xuan Zhang | Shuming Jiao | Shiwei Sun | Longyang Dian | Xuefeng Cui
Source:  (article, 2024-12-03)
Field: Chemistry — Spectroscopy

Deep Representation Learning for Electron Ionization Mass Spectra Retrieval The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI’s 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://github.com/xfcui/EI-MS2VEC). Ionization ; Electron ; Computer science ; Spectral line ; Representation (politics) ; Mass spectrum ; Artificial intelligence ; Physics ; Atomic physics ; Mass spectrometry ; Ion ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Shiyi Zhang | Qing Zhang | Ran Xu | Xing Wu | Yan Wang | Chaolun Wang
Source:  (article, 2024-07-15)
Field: Materials Science — Surfaces, Coatings and Films

Denoising of Transmission Electron Microscopy Images for Atomic Defect Identification Two-dimensional (2D) materials possess remarkable electrical, mechanical, thermal, and optical properties, making them applicable in the fields of electronics and optoelectronics. Atomic defects could significantly affect device performance and reliability. Clearly identify and analysis of atomic defects in 2D materials is crucial. Transmission electron microscopy (TEM) with atomic resolution serves as an ideal method for the defect characterization of 2D materials. However, TEM images often suffer from various types of noise, which severely hampers the analysis of 2D materials. In this study, we propose a neural network based on the optimized CycleGAN architecture for TEM image denoising even when obtaining strictly paired datasets for TEM is challenging. The optimized CycleGAN model with attention gate could improve the signal-to-noise ratio and structural similarity of the denoised images. The effectiveness of this method was validated on the scanning transmission electron microscopy image dataset. Enhancing the visibility of atomic contrast and improving the accuracy of identifying individual atomic structures are achieved by removing the surrounding noise from the original image. Transmission electron microscopy ; Identification (biology) ; Electron microscope ; Computer science ; Microscopy ; Artificial intelligence ; Scanning transmission electron microscopy ; Materials science ; Electron tomography ; Computer vision ; Pattern recognition (psychology) ; Optoelectronics ; Optics ; Physics ; Nanotechnology ; Biology Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques
Authors: Ying Jing | Yanli Zhang | Dianhai Zhang | Jianguo Zhu
Source:  (article, 2024-06-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Calculation of Iron Loss in Soft Magnetic Composites Using Neural Network-Based Dynamic Hysteresis Model Under SVPWM Excitation Soft Magnetic Composite (SMC) materials, serving as key components in high-power motors, exhibit a loss mechanism distinctly different from traditional laminated core materials like silicon steel. To predict these losses more accurately, this study introduces a dynamic hysteresis model based on Neural Networks (NN) and trapezoidal equivalent circuits, designed to calculate the iron losses of SMC under Space Vector Pulse Width Modulation (SVPWM) excitation. A NN algorithm, capable of characterizing the magnetization process, simulates the static hysteresis component. Meanwhile, when calculating eddy current losses and anomalous losses using trapezoidal equivalent circuits, the effect of skin effect on iron losses is taken into account. The model's accuracy and effectiveness are validated through a comparison of theoretical and experimental results. This model enhances the application of SMC in motors and provides robust support for the optimization of motor design. Hysteresis ; Excitation ; Materials science ; Magnetic hysteresis ; Artificial neural network ; Composite material ; Computer science ; Magnetic field ; Physics ; Electrical engineering ; Magnetization ; Engineering ; Condensed matter physics ; Artificial intelligence Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties of Alloys
Authors: Guorong Qiao | Jinrong Chen
Source:  (article, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Application and research of clothing fabric identification based on cloud, edge and end intellisense Spectrum is the fingerprint of substance. Using spectrum analysis technology to identify substance will open up a new dimension of information for artificial intelligence and perception technology, which has a huge application demand in many fields. The project team uses the wavelet transform near infrared spectrum (WTNIR) method to complete the analysis of clothing fabrics. Due to the good characteristics of wavelet transform, it is widely used in the field of graphics and image processing, and achieves better practical results than the original technology. By studying the basic theory of wavelet transform, the wavelet transform theory is applied to image processing and overcomes the defects of short-time Fourier transform in single resolution. It has the characteristics of multi-resolution analysis, and has the ability to represent local information of signals in both time and frequency domains. Using wavelet transform multi-scale edge detection image segmentation method can clearly distinguish the yarn density of clothing fabrics, so as to better identify the quality of the fabric. This project uses decision tree to build material classification model by the spectroscopic data which we collected, uses U-net to build a neural network to distinguish the yarn density, uses small sample transfer learning detection technology to complete clothing color distinction, and uses Edge AI small model training and the method in the transfer learning to improve the performance and efficiency of the neural network on the terminal target hardware. Through experiments, it is found that the material composition of clothing fabrics can be accurately analyzed at the spectral band of about 1500 nm, which provides a basis for the subsequent development of hand-held clothing fabric analyzer. Computer science ; Wavelet transform ; Artificial intelligence ; Computer vision ; Wavelet ; Pattern recognition (psychology) ; Clothing ; Edge detection ; Enhanced Data Rates for GSM Evolution ; Image processing ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nicolas Hayer | Thorsten Wendel | Stephan Mandt | Hans Hasse | Fabian Jirasek
Source: arXiv (Cornell University) (preprint, 2024-07-25)
Field: Materials Science — Materials Chemistry

Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0 Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods are widely employed for this purpose but suffer from historically grown, incomplete parameterizations, limiting their applicability and accuracy. In this work, we overcome these limitations by combining GC with matrix completion methods (MCM) from machine learning. We use the novel approach to predict a complete set of pair-interaction parameters for the most successful GC method: UNIFAC, the workhorse for predicting activity coefficients in liquid mixtures. The resulting new method, UNIFAC 2.0, is trained and validated on more than 224,000 experimental data points, showcasing significantly enhanced prediction accuracy (e.g., nearly halving the mean squared error) and increased scope by eliminating gaps in the original model's parameter table. Moreover, the generic nature of the approach facilitates updating the method with new data or tailoring it to specific applications. UNIFAC ; Group (periodic table) ; Thermodynamics ; Group contribution method ; Phase equilibrium ; Chemistry ; Physical chemistry ; Activity coefficient ; Physics ; Organic chemistry Machine Learning in Materials Science
Authors: Kamyar Barakati | Utkarsh Pratiush | Austin Houston | Gerd Duscher | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2024-09-19)
Field: Materials Science — Surfaces, Coatings and Films

Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation. Currently, segmentation tasks are typically performed using supervised machine learning methods, which require human-labeled data and are sensitive to out-of-distribution drift effects caused by changes in resolution, sampling, or beam shape. Here, we operationalize and benchmark a recently proposed reward-driven optimization workflow for on-the fly image analysis in STEM. This unsupervised approach is much more robust, as it does not rely on human labels and is fully explainable. The explanatory feedback can help the human to verify the decision making and potentially tune the model by selecting the position along the Pareto frontier of reward functions. We establish the timing and effectiveness of this method, demonstrating its capability for real-time performance in high-throughput and dynamic automated STEM experiments. The reward driven approach allows to construct explainable robust analysis workflows and can be generalized to a broad range of image analysis tasks in electron and scanning probe microscopy and chemical imaging. Artificial intelligence ; Segmentation ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Computer vision ; Electron tomography ; Image (mathematics) ; Computer science ; Scanning electron microscope ; Pattern recognition (psychology) ; Image segmentation ; Materials science ; Scanning confocal electron microscopy ; Microscopy ; Transmission (telecommunications) ; Optics ; Nanotechnology ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Shufei Zhang | Taoyong Cui | Chenyu Tang | D.C. Zhou | Yuqiang Li | Xingao Gong | Wanli Ouyang | Mao Su
Source: Research Square (preprint, 2024-06-14)
Field: Materials Science — Materials Chemistry

Online Test-time Adaptation for InteratomicPotentials Computer science ; Adaptation (eye) ; Generalization ; Molecular dynamics ; Test data ; Bridge (graph theory) ; Test (biology) ; Domain adaptation ; Interatomic potential ; Mathematics ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics