jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ghane Moradkhani | Jacopo Profili | Alex Destrieux | Mathieu Robert | Gaétan Laroche | Saïd Elkoun | Frej Mighri | Pascal Y. Vuillaume
Source: Journal of Composites Science (article, 2024-03-18)
Field: Materials Science — Biomaterials

Plasma-Enhanced Alginate Pre-Treatment of Short Flax Fibers for Improved Thermo-Mechanical Properties of PLA Composites This research centered on enhancing the mechanical properties of sustainable composite materials made from short flax fibers. Challenges associated with fiber–matrix adhesion and moisture absorption were systematically addressed. A water–alginate pre-treatment, combined with plasma modification, was employed to stabilize the fibers, ensuring their optimal preparation and improved compatibility with biopolymers. A thorough investigation of the effect of the plasma modulation using a duty cycle (DC) was conducted, and extensive physicochemical and mechanical analyses were performed. These efforts revealed conditions that preserved fiber integrity while significantly improving surface characteristics. Techniques such as optical emission spectroscopy (OES), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and Dynamic Mechanical Analysis (DMA) were utilized, providing a comprehensive understanding of the transformations induced by the plasma treatment. The findings underscored the critical role of alginate and precise plasma settings in enhancing the mechanical properties of the composites. Ultimately, this study made a substantial contribution to the field of eco-friendly materials, showcasing the potential of short flax fibers in sustainable composite applications and setting the stage for future advancements in this area. Composite material ; Materials science biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Chenrui Niu | X. Ying | Gan Pei | Menghan Hu | Guangtao Zhai
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Review of the deep learning for food image processing As deep learning techniques are increasingly applied with greater depth and sophistication the food industry, the realm of food image processing has progressively emerged as a central focus of research interest. This work provides an overview of key practices in food image processing techniques, detailing common processing tasks including classification, recognition, detection, segmentation, and image retrieval, as well as outlining metrics for evaluating task performance and thoroughly examining existing food image datasets, along with specialized food-related datasets. In terms of methodology, this work offers insight into the evolution of food image processing, tracing its development from traditional methods extracting low and intermediate-level features to advanced deep learning techniques for high-level feature extraction, along with some synergistic fusion of these approaches. It is believed that these methods will play a significant role in practical application scenarios such as self-checkout systems, dietary health management, intelligent food service, disease etiology tracing, chronic disease management and food safety monitoring. However, due to the complex content and various types of distortions in food images, further improvements in related methods are needed to meet the requirements of practical applications in the future. It is believed that this study can help researchers to further understand the research in the field of food imaging and provide some contribution to the advancement of research in this field. Keywords: deep learning, food image processing, feature extraction, dietary health DOI: 10.25165/j.ijabe.20241705.8975 Citation: Niu C R, Ying X Y, Pei G, Hu M H, Zhai G T. Review of the deep learning for food image processing. Int J Agric & Biol Eng, 2024; 17(5): 14-29. Artificial intelligence ; Computer vision ; Image processing ; Computer science ; Image (mathematics) ; Food science ; Agricultural engineering ; Biology ; Engineering Spectroscopy and Chemometric Analyses
Authors: Vo-Nguyen Tuyet-Doan | Young-Woo Youn | Hyun-Soo Choi | Yong‐Hwa Kim
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear Recently, deep neural networks have shown remarkable success in fault diagnosis in power systems using partial discharges (PDs), thereby enhancing grid asset safety and reliability. However, the prevailing approaches often adopt centralized large-scale datasets for training, without taking into account the impact of noise environments for Intelligent Electronic Devices (IEDs). Noise environments for PD measurements in gas-insulated switchgear (GIS) introduce variations in feature distributions and class representations, challenging the generalization ability of the trained models in new and diverse conditions. In this study, we propose a Shared Knowledge-based Contrastive Federated Learning (SK-CFL) for PD diagnosis in different noise environments for IEDs. The proposed SK-CFL combines federated learning principles with contrastive learning, empowering IEDs to collaboratively learn and share knowledge as regards PD and noise patterns. The proposed framework can learn representations between the same patterns across different IEDs while ensuring data privacy. Experimental results for PD diagnosis in GIS show that the proposed SK-CFL achieves a performance improvement in fault diagnosis, particularly in new and unseen environments. Specifically, the recall for unknown noise in untrained IED 6 demonstrates 92.86% of the proposed SK-CFL, in comparison with 64.29% and 35.71% of the conventional FL and baseline method, respectively. These results suggest that the proposed SK-CFL approach promises more adaptable, and resilient data-driven approaches since it protects data privacy that can operate effectively in challenging real-world environments. Switchgear ; Computer science ; Noise (video) ; Reliability (semiconductor) ; Generalization ; Feature learning ; Fault (geology) ; Partial discharge ; Artificial intelligence ; Machine learning ; Power (physics) ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Geophysical Methods and Applications ; Electrical Fault Detection and Protection
Authors: Xin Liu | Kaixin Meng | Kaixing Zhang | Wujie Yang | Jiutao Yang | Lingyang Feng | Haoran Gong | Chang’an Zhou
Source: Frontiers in Plant Science (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production. Hyperspectral imaging ; Preprocessor ; Intercropping ; Pattern recognition (psychology) ; Support vector machine ; Artificial intelligence ; Principal component analysis ; Computer science ; Mathematics ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Shrabani Dinda | Tanvi Bhola | Suyash Pant | Anand Chandrasekaran | Alex K. Chew | Mathew D. Halls | M. I. S. Sastry
Source: Journal of Catalysis (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Machine learning-based design of pincer catalysts for polymerization reaction Chemistry ; Pincer movement ; Catalysis ; Polymerization ; Combinatorial chemistry ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Organometallic Complex Synthesis and Catalysis
Authors: Paulina Baczewska | Michał Kulczykowski | Bartosz K. Zambroń | Joanna A. Jaszczewska‐Adamczak | Zbigniew Pakulski | Rafał Roszak | Bartosz A. Grzybowski | Jacek Młynarski
Source: Angewandte Chemie (article, 2024-06-15)
Field: Chemistry — Inorganic Chemistry

Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions Abstract Organic‐chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts’ scope but do not necessarily guarantee that a given catalyst is “optimal”—in terms of yield or enantiomeric excess—for a particular reaction. This paper describes a Machine Learning model that aims to improve such catalyst‐reaction assignments based on the carefully curated literature data. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of‐the‐box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions. Catalysis ; Scope (computer science) ; Yield (engineering) ; Magnesium ; Computer science ; Chemistry ; Enantioselective synthesis ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Physics ; Thermodynamics ; Engineering Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Claudio Ronchetti | Sara Marchio | Francesco Buonocore | Simone Giusepponi | Sergio Ferlito | Massimo Celino
Source: Batteries (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods. Density functional theory ; Computer science ; Battery (electricity) ; Cathode ; Convolutional neural network ; Workflow ; Energy density ; Ion ; Lithium (medication) ; Graph theory ; Energy storage ; Materials science ; Artificial neural network ; Nanotechnology ; Artificial intelligence ; Engineering physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Peiyuan Liu | Xiaoyang Zhu | Xu Ran | Hengchang Bi | Xiao Huang | Ning Gu
Source: Coordination Chemistry Reviews (article, 2024-11-15)
Field: Materials Science — Materials Chemistry

Machine learning for gas–solid interaction materials and devices Chemistry ; Nanotechnology ; Chemical engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Nuclear Physics and Applications
Authors: Sandesh Risal | Navdeep Singh | Yan Yao | Li Sun | Samprash Risal | Weihang Zhu
Source: Materials (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy's composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys. Materials science ; Material properties ; Elastic modulus ; Crystal structure prediction ; Interatomic potential ; Molecular dynamics ; Work (physics) ; Machine learning ; Shear modulus ; Coupling (piping) ; Experimental data ; Algorithm ; Property (philosophy) ; Computer science ; Lattice (music) ; Statistical physics ; Crystal structure ; Thermodynamics ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Crystallography ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mohammad Rezasefat | James D. Hogan
Source: Virtual and Physical Prototyping (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning Additive manufacturing offers a solution for producing advanced ceramics with complex geometries by enabling precise control over geometry, microstructure, and composition. By leveraging deep learning, rapid prototyping and evaluation of printed ceramic parts become feasible. This study employs convolutional neural networks and transfer learning to predict spatiotemporal fields in ceramics, using synthetic datasets generated from X-ray computed tomography (micro-CT) and finite element analysis. The novel approach integrates spatiotemporal factors into deep learning models, enhancing the prediction of stress and damage evolution, and ultimately providing deeper insights into material behaviour and performance. Additionally, we introduce a target loss training strategy, which focuses on achieving a specific accuracy level, thus reducing training time while maintaining high precision. The proposed deep learning framework achieves accurate predictions of spatiotemporal stress fields, and captures fracture initiation and propagation behaviours. This method facilitates rapid prototyping and advances material design and evaluation processes while significantly reducing experimental efforts. Transfer of learning ; Convolutional neural network ; Deep learning ; Rapid prototyping ; Stress (linguistics) ; Artificial intelligence ; Artificial neural network ; Computer science ; Ceramic ; Materials science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: YiRu Wen | Jia Fu | Tongan Yan | Dahuan Liu
Source: Inorganic Chemistry Communications (article, 2024-07-29)
Field: Chemistry — Inorganic Chemistry

Combining machine learning and molecular simulation to explore MOF materials that contribute to CF4/N2 separation Separation (statistics) ; Materials science ; Nanotechnology ; Computer science ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Gas Sensing Nanomaterials and Sensors
Authors: Ayesh Madushanka | Renaldo T. Moura | Elfi Kraka
Source: Scientific Data (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

QM40, Realistic Quantum Mechanical Dataset for Machine Learning in Molecular Science The growing popularity of machine learning (ML) and deep learning (DL) in scientific fields is hindered by the scarcity of high-quality datasets. While quantum mechanical (QM) predictions using DL techniques such as graph neural networks (GNNs) and generative models are gaining traction, insufficient training data remains a bottleneck. The QM40 dataset addresses this challenge by representing 88% of the FDA-approved drug chemical space. It includes molecules containing 10 to 40 atoms and composed of elements commonly found in drug molecular structures (C, O, N, S, F, Cl). QM40 offers valuable resources for researchers which include the core QM40 main dataset, containing 16 key quantum mechanical parameters for 162,954 molecules calculated using the B3LYP/6-31G(2df,p) level of theory in Gaussian16, ensuring consistency with established datasets like QM9 and Alchemy. This compatibility allows for future concatenation of QM40 with these datasets. In addition to other valuable information, the QM40 dataset offers the initial and optimized Cartesian coordinates, Mulliken charges, and detailed bond information, including local vibrational mode force constants, which serve as indicators of bond strength. QM40 can be used to benchmark both existing and new methods for predicting QM calculations using ML and DL techniques. Chemical space ; Computer science ; Bottleneck ; Quantum chemistry ; Quantum ; Machine learning ; Artificial intelligence ; Drug discovery ; Molecule ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shubin Liu
Source: ACS Physical Chemistry Au (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

Harvesting Chemical Understanding with Machine Learning and Quantum Computers It is tenable to argue that nobody can predict the future with certainty, yet one can learn from the past and make informed projections for the years ahead. In this Perspective, we overview the status of how theory and computation can be exploited to obtain chemical understanding from wave function theory and density functional theory, and then outlook the likely impact of machine learning (ML) and quantum computers (QC) to appreciate traditional chemical concepts in decades to come. It is maintained that the development and maturation of ML and QC methods in theoretical and computational chemistry represent two paradigm shifts about how the Schrödinger equation can be solved. New chemical understanding can be harnessed in these two new paradigms by making respective use of ML features and QC qubits. Before that happens, however, we still have hurdles to face and obstacles to overcome in both ML and QC arenas. Possible pathways to tackle these challenges are proposed. We anticipate that hierarchical modeling, in contrast to multiscale modeling, will emerge and thrive, becoming the workhorse of <i>in silico</i> simulations in the next few decades. nobody ; Computer science ; Function (biology) ; Perspective (graphical) ; Artificial intelligence ; Face (sociological concept) ; Computation ; Data science ; Theoretical computer science ; Algorithm ; Sociology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Atmika Bhardwaj | Jens‐Uwe Sommer | Marco Werner
Source: Macromolecules (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models We use machine learning algorithms to detect the crystalline phase in undercooled melts simulated via molecular dynamics. Our classification method relies only on the analysis of local conformation and environmental fingerprints of individual monomers. We employ self-supervised autoencoders to compress the fingerprint information, coupled with a Gaussian mixture model to distinguish ordered states from disordered ones. The method does not require explicit information on the expected phases in the system, but automatically detects the local signatures of alignment and stretched conformations in the neighborhood of the monomer, and thereby determines a decision boundary between two classes. We demonstrate that the classes identified by the method correspond to a large extent with the result of classifiers based on human-intuitive order parameters such as the stem length [Luo, C.; Sommer, J.-U. Macromolecules 2011, 44, 1523]. We also show that the machine learning result may facilitate the determination of decision boundaries for simpler classifiers that are commonly used for such systems. In the present system, we thereby enhance the resolution of detailed time patterns of crystalline order before an apparent signature of the transition is visible in thermodynamic properties. At a time point before the transition is manifested in the specific volume and specific heat, a sudden stabilization of monomers in the crystalline phase is observed. Nucleation ; Polymer ; Materials science ; Polymer science ; Chemical physics ; Polymer chemistry ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Engineering Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods