jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stijn D'Hondt | José Oramas | Hans De Winter
Source: Journal of Cheminformatics (review, 2025-04-08)
Field: Materials Science — Materials Chemistry

A beginner’s approach to deep learning applied to VS and MD techniques It has become impossible to imagine the fields of biochemistry and medicinal chemistry without computational chemistry and molecular modelling techniques. In many steps of the drug development process in silico methods have become indispensable. Virtual screening (VS) can tremendously expedite the early discovery phase, whilst the use of molecular dynamics (MD) simulations forms a powerful additional tool to in vitro methods throughout the entire drug discovery process. In the field of biochemistry, MD has also become a compelling method for studying biophysical systems (e.g., protein folding) complementary to experimental techniques. However, both VS and MD come with their own limitations and methodological difficulties, from hardware limitations to restrictions in algorithmic capabilities. One solution to overcoming these difficulties lies in the field of machine learning (ML), and more specifically deep learning (DL). There are many ways in which DL can be applied to these molecular modelling techniques to achieve more accurate results in a more efficient manner or expedite the data analysis of the acquired results. Despite steadily increasing interest in DL amidst computational chemists, knowledge is still limited and scattered over different resources. This review is aimed at computational chemists with knowledge of molecular modelling, who wish to possibly integrate DL approaches in their research and already have a basic understanding of the fundamentals of DL. This review focusses on a survey of recent applications of DL in molecular modelling techniques. The different sections are logically subdivided, based on where DL is integrated in the research: (1) for the improvement of VS workflows, (2) for the improvement of certain workflows in MD simulations, (3) for aiding in the calculations of interatomic forces, or (4) for data analysis of MD trajectories. It will become clear that DL has the capacity to completely transform the way molecular modelling is carried out. Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Oil and Gas Production Techniques
Authors: Tiago Pinheiro Ribeiro | Paulo Roberto Duailibe Monteiro | Márcio Zamboti Fortes | Thiago Trezza Borges
Source: Journal of Engineering Science and Technology Review (review, 2025-06-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Offshore Substations: Key Aspects and Trends Offshore Substations (OS) are critical infrastructure components in energy delivery, particularly for offshore wind farms and other marine renewable energy (RE) sources.They are essential for integrating these renewable sources into the power grid.This work provides a comprehensive overview of recent developments in OS through a systematic literature review and bibliometric analysis.The research process involved identifying relevant academic publications from databases such as Scopus and Web of Science, using specific keywords related to OS.A temporal limitation was set, including materials published from 2020 to July 2024.Initial results were filtered to remove duplicates, and after analyzing titles and abstracts, 122 publications were selected for in-depth analysis.This methodology ensured a thorough examination of the technological evolution, advancements in design, materials, and applied technologies.The article also explores the integration of OS with RE sources, such as wind farms, emphasizing success stories and challenges.Key construction aspects, including modular designs, foundations, and construction techniques, are summarized.Best practices for operation and maintenance are reviewed to ensure the longevity and efficiency of OS.Finally, this paper analyzes future trends, addressing emerging technologies like floating substations and their potential integration with other technologies.By summarizing these key aspects, this study aims to serve as a resource for future research and development in the field of OS. Key (lock) ; Submarine pipeline ; Environmental science ; Engineering ; Marine engineering ; Computer science ; Oceanography ; Geology ; Computer security High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; High-Voltage Power Transmission Systems
Authors: Anqi Gao | Xiaofu Wang | Erhu Guo | Dongxu Zhang | Kai Cheng | Xiaoguang Yan | Guoliang Wang | Aiying Zhang
Source: Foods (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

Rapid and Non-Destructive Assessment of Eight Essential Amino Acids in Foxtail Millet: Development of an Efficient and Accurate Detection Model Based on Near-Infrared Hyperspectral Foxtail millet is a vital grain whose amino acid content affects nutritional quality. Traditional detection methods are destructive, time-consuming, and inefficient. This work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet. To address these limitations, this study developed a rapid, non-destructive approach for quantifying eight essential amino acids-lysine, phenylalanine, methionine, threonine, isoleucine, leucine, valine, and histidine-in foxtail millet (variety: Changnong No. 47) using near-infrared hyperspectral imaging. A total of 217 samples were collected and used for model development. The spectral data were preprocessed using Savitzky-Golay, adaptive iteratively reweighted penalized least squares, and standard normal variate. The key wavelengths were extracted using the competitive adaptive reweighted sampling algorithm, and four regression models-Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)-were constructed. The results showed that the key wavelengths selected by CARS account for only 2.03-4.73% of the full spectrum. BiLSTM was most suitable for modeling lysine (R<sup>2</sup> = 0.5862, RMSE = 0.0081, RPD = 1.6417). CNN demonstrated the best performance for phenylalanine, methionine, isoleucine, and leucine. SVR was most effective for predicting threonine (R<sup>2</sup> = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains. Hyperspectral imaging ; Partial least squares regression ; Foxtail ; Support vector machine ; Regression ; Convolutional neural network ; Mean squared error Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food composition and properties
Authors: Alireza Sanaeifar | Ruth Dill‐Macky | Rebecca D. Curland | Susan Reynolds | Matthew N. Rouse | Shahryar F. Kianian | Ce Yang
Source: Remote Sensing (article, 2025-08-13)
Field: Chemistry — Analytical Chemistry

High-Throughput UAV Hyperspectral Remote Sensing Pinpoints Bacterial Leaf Streak Resistance in Wheat Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R2 of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Hyperspectral imaging ; Streak ; Remote sensing ; Throughput ; Environmental science ; Computer science ; Geology ; Mineralogy ; Telecommunications Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Mycotoxins in Agriculture and Food
Authors: Chen Li | Yu Wu | Ning Yang | Zongbao Sun
Source: Agriculture (article, 2025-08-19)
Field: Chemistry — Analytical Chemistry

Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications Hyperspectral imaging and diffraction imaging technologies, owing to their non-destructive nature, high efficiency, and superior resolution, have found widespread application in agricultural diagnostics. This review synthesizes recent advancements in the deployment of these two technologies across various agricultural domains, including the detection of plant diseases and pests, crop growth monitoring, and animal health diagnostics. Hyperspectral imaging utilizes multi-band spectral and image data to accurately identify diseases and nutritional status, while combining deep learning and other technologies to improve detection accuracy. Diffraction imaging, by exploiting the diffraction properties of light waves, facilitates the detection of pathogenic spores and the assessment of cellular vitality, making it particularly well-suited for microscopic structural analysis. The paper also critically examines prevailing challenges such as the complexity of data processing, environmental adaptability, and the cost of instrumentation. Finally, it envisions future directions wherein the integration of hyperspectral and diffraction imaging, through multisource data fusion and the optimization of intelligent algorithms, holds promise for constructing highly precise and efficient agricultural diagnostic systems, thereby advancing the development of smart agriculture. Hyperspectral imaging ; Agriculture ; Environmental science ; Remote sensing ; Geography ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Marco Mingrone | Marco Seracini | Chiara Cevoli
Source: Applied Sciences (article, 2025-10-13)
Field: Chemistry — Analytical Chemistry

Spectral Reconstruction Applied in Precision Agriculture: On-Field Solutions Over the past two decades, hyperspectral imaging (HSI) systems have shown significant potential in agriculture, from disease detection to the assessment of plant and fruit nutritional status. However, most applications remain confined to laboratory analyses under controlled conditions, with only a limited fraction implemented in field environments. In this scenario, spectral reconstruction techniques may serve as a bridge between the high accuracy of HSI and the challenges of on-field or even real-time applications. This review outlines the current state of the art of on-field HSI in the agrifood sector, highlighting existing limitations and potential advantages. It then introduces the problem of spectral reconstruction and reviews current techniques used to address it. Laboratory and on-field studies will be taken into account. The final section offers our perspective on the limitations of HSI and the promising potential of spectral super-resolution to overcome current barriers and enable broader adoption of hyperspectral technology in precision agriculture. Hyperspectral imaging ; Perspective (graphical) ; Current (fluid) ; Field (mathematics) ; Spectral analysis Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Zeqing Yang | Jiahui Zhang | Zhimeng Li | Ning Hu | Zhengpan Qi
Source: Agriculture (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. Artificial intelligence ; Computer science ; Convolutional neural network ; Classifier (UML) ; Pattern recognition (psychology) ; Histogram ; Local binary patterns ; Grayscale ; Computer vision ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Date Palm Research Studies
Authors: Xiangpeng Fan | Jianping Zhou
Source: Foods (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection and Quality Grading System of Walnut Using X-Ray Imaging and Lightweight WKNet The internal quality detection is extremely important. To solve the challenges of walnut quality detection, we presented the first comprehensive investigation of walnut quality detection method using X-ray imaging and deep learning model. An X-ray machine vision system was designed, and a walnut kernel detection (called WKD) dataset was constructed. Then, an effective walnut kernel detection network (called WKNet) was developed by employing Transformer, GhostNet, and criss-cross attention (called CCA) module to the YOLO v5s model, aiming to solve the time consuming and parameter redundancy issues. The WKNet achieved an mAP_0.5 of 0.9869, precision of 0.9779, and recall of 0.9875 for walnut kernel detection. The inference time per image is only 11.9 ms. Extensive comparison experiments with the state-of-the-art (SOTA) deep learning models demonstrated the advanced nature of WKNet. The online test of walnut internal quality detection also shows satisfactory performance. The innovative combination of X-ray imaging and WKNet provide significant implications for walnut quality control. Artificial intelligence ; Computer science ; Kernel (algebra) ; Pattern recognition (psychology) ; Deep learning ; Machine vision ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Identification and Quantification in Food
Authors: Qi Yuan | Tan Liu | Songlin Guo | Peiyan Wu | Jun Ma | Qingyun Yuan | Weixiang Yao | Tongyu Xu
Source: Agriculture (article, 2025-08-02)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Magnaporthe grisea ; Kappa ; Convolutional neural network ; Mathematics ; Oryza sativa Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Alireza Mashayekhi | Sepehr Khazraei | Jack Bekou
Source: Applied Catalysis A General (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Machine learning for catalyst optimization: Outlier detection and material innovation Chemistry ; Catalysis ; Heterogeneous catalysis ; Biochemical engineering ; Chemical engineering ; Process engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Alea Miako Tokita | Timothée Devergne | A. Marco Saitta | Jörg Behler
Source: The Journal of Chemical Physics (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis Machine learning potentials (MLPs) have become a popular tool in chemistry and materials science as they combine the accuracy of electronic structure calculations with the high computational efficiency of analytic potentials. MLPs are particularly useful for computationally demanding simulations such as the determination of free energy profiles governing chemical reactions in solution, but to date, such applications are still rare. In this work, we show how umbrella sampling simulations can be combined with active learning of high-dimensional neural network potentials (HDNNPs) to construct free energy profiles in a systematic way. For the example of the first step of Strecker synthesis of glycine in aqueous solution, we provide a detailed analysis of the improving quality of HDNNPs for datasets of increasing size. We find that, in addition to the typical quantification of energy and force errors with respect to the underlying density functional theory data, the long-term stability of the simulations and the convergence of physical properties should be rigorously monitored to obtain reliable and converged free energy profiles of chemical reactions in solution. Artificial neural network ; Convergence (economics) ; Stability (learning theory) ; Computer science ; Energy (signal processing) ; Chemistry ; Construct (python library) ; Aqueous solution ; Work (physics) ; Strecker amino acid synthesis ; Chemical stability ; Computational chemistry ; Biological system ; Artificial intelligence ; Machine learning ; Thermodynamics ; Mathematics ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Na Qin | Wenxin Guo | Fei Li | Houfeng Zhang | Liu Hong | Chang Zhang | Lipiao Bao | Lei Liu | Muneerah Alomar | Siqi Zhao | Jian Zhang | Xing Lu
Source: Chinese Chemical Letters (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction Alloy ; Catalysis ; Clean energy ; Component (thermodynamics) ; Electrocatalyst ; Characterization (materials science) ; Hydrogen ; Water splitting Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Eleonora Casillo | Thomas Scattolin | Steven P. Nolan
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Catalysis meets machine learning: a guide to data-driven discovery and design Machine learning (ML) has rapidly become an indispensable tool across the chemical sciences, offering powerful methods to extract patterns from data and make accurate predictions in complex, multidimensional systems. In organometallic catalysis, its potential is particularly evident: while transition-metal catalysed reactions are at the core of modern synthesis, their design and optimization remain challenging due to the vastness of chemical space, the scarcity of standardized data, and the intricate interplay of steric, electronic, and mechanistic factors. This review aims to provide chemists with both a conceptual and practical entrypoint into the field, beginning with a concise introduction to the principles of ML and its most widely used algorithms. It then surveys recent advances by structuring the discussion according to applications: optimization of reaction conditions, mechanistic elucidation, ligand classification and design, stereocontrol, and the discovery of novel catalysts. By combining methodological insights with case studies, we highlight how ML can reduce experimental workload, enhance mechanistic understanding, and guide rational catalyst development, while also outlining current limitations and future opportunities at the interface of data science and catalysis. Structuring ; Scarcity ; Interface (matter) ; Core (optical fiber) ; Rational design Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Kentaro Miyamoto | Kōji Shimizu | Anh Khoa Augustin Lu | Satoshi Watanabe
Source: e-Journal of Surface Science and Nanotechnology (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Construction of Machine Learning Potentials toward the Exploration of Alloy Cluster Catalysts High entropy alloys (HEAs) are expected to show excellent performance in various fields, such as catalysts and high-temperature structural materials, but the huge number of configurations makes it difficult to find the optimal compositions for HEAs. In this study, machine learning potentials were developed to accurately predict the total and H/CO adsorption energies of multi-element slab models and cluster models of various sizes and shapes, based on density functional theory calculations. Cluster (spacecraft) ; Alloy ; Catalysis ; Psychology ; Computer science ; Materials science ; Chemistry ; Metallurgy ; Operating system ; Organic chemistry Machine Learning in Materials Science