jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuejun Wu | Xuemei Wu | Delun Li | Feng Wang | Fugui Zhang | Yang Cao
Source: Analytical Letters (article, 2023-11-23)
Field: Chemistry — Analytical Chemistry

Tobacco Leaves Maturity Classification Based on Deep Learning and Proximal Hyperspectral Imaging Accurately measuring tobacco harvest maturity is crucial for optimizing quality and crop management. Traditional methods heavily rely on qualitative evaluation and chemical experiments, which introduce subjectivity and inherent limitations. This study proposes a novel method that combines deep learning with proximal hyperspectral imaging (HSI) to achieve precise recognition of tobacco leaf maturity. Unlike traditional techniques, HSI captures rich spectral and spatial data, enabling comprehensive analysis. It adopts a pixel-level annotation strategy to annotate each pixel based on its maturity level, thereby preserving spectral complexity. The dataset comprises 3000 randomly extracted cubes (5×5×176) from 150 original hyperspectral images, encompassing five maturity levels. Spectral and spatial features are extracted from hyperspectral data using a three-dimensional convolutional neural network (3D-CNN) architecture. This method effectively leverages complex spectral patterns for maturity recognition. During testing, the model demonstrated an impressive average accuracy of 99.93%. Visual predictions vividly illustrate the model's proficiency in maturity recognition, affirming its practical utility. This study pioneers the integration of deep learning and hyperspectral near-end sensing technology in tobacco maturity assessment, mitigating the constraints of traditional methods, and establishing the groundwork for real-time monitoring and quality control of tobacco. Hyperspectral imaging ; Convolutional neural network ; Maturity (psychological) ; Artificial intelligence ; Computer science ; Multispectral image ; Deep learning ; Pixel ; Pattern recognition (psychology) ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Justin Airas | Xinqiang Ding | Bin Zhang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up. Granularity ; Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced machining processes and optimization
Authors: Martin Jacob | Ali Hallal | Julien Baderot | Vincent Barra | Arnaud Guillin | Sergio Martinez | J. Foucher
Source:  (preprint, 2023-04-18)
Field: Materials Science — Surfaces, Coatings and Films

A generic deep-learning-based defect segmentation model for electron micrographs for automatic defect inspection Defect inspection is an important part in the semiconductor manufacturing. This task is tedious and time consuming if done manually. Therefore, reliably automating this task is a major challenge for many semiconductor manufacturers. In the recent years, deep-learning methods for object detection have demonstrated ever better performances. However, most of the publicly available models are trained on natural images and objects. Hence, most of them needs a long and data greedy training step to be used on industrial Transmission Electron Microscopes or Scanning Electron Microscope images. In this context, we propose a deep-learning based model to detect and segment defects in electron micrographs. Using SmartDef3 from Pollen Metrology, we annotated defects on images from several industrial applications. We split them in a training and validation dataset, with which an Instance segmentation model with state-of-the-art backbone is trained. The model is then evaluated on different use cases. Competitive performances on new data in terms of detection rates and segmentation quality are demonstrated and discussed. Furthermore, the model showed a relevant defect detection rate even on images that are not in the semiconductor domain, providing an interesting tool for defects detection on a new use case without new training data. This shows how deep learning strategies can help save time and costs by automation of defects inspections. Furthermore, advanced metrological analysis of the defect can be simultaneously obtained that help optimizing the manufacturing processes and reduce defect production rate. Computer science ; Artificial intelligence ; Segmentation ; Deep learning ; Context (archaeology) ; Automation ; Semiconductor device fabrication ; Task (project management) ; Image segmentation ; Computer vision ; Metrology ; Pattern recognition (psychology) ; Machine learning ; Engineering ; Optics ; Wafer Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: Aleksandr Fedorov | Anna Perechodjuk | David Linke
Source: ChemRxiv (preprint, 2023-05-14)
Field: Materials Science — Materials Chemistry

Kinetics-Constrained Neural Ordinary Differential Equations: Artificial Neural Network Models tailored for Small Data to boost Kinetic Model Development Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are available. In the present work we propose a method to design ANN models for kinetic modelling that can be trained even with small data sets as are typically available. The key idea is to constrain the architecture of the ANN models by integrating kinetic and thermodynamic knowledge leading to what we call Kinetics-Constrained Neural Ordinary Differential Equations (KCNODE). The feasibility and effectiveness of the approach is first demonstrated in a numerical experiment using the catalytic hydrogenation of CO2 to methane as example. Next, we demonstrate the approach for real experimental data of a more complex reaction, the hydrogenation of CO2 to higher hydrocarbons (CO2-FT). Finally, the ANN trained for CO2-FT is used to derive an improved mechanistic model for the reverse water gas shift reaction which is a key reaction in the CO2-FT reaction network. This last step exemplifies how the opportunity to obtain reliable ANN models from small data opens new ways to approach kinetic model development. Artificial neural network ; Ordinary differential equation ; Computer science ; Key (lock) ; Range (aeronautics) ; Experimental data ; Kinetic energy ; Small data ; Artificial intelligence ; Machine learning ; Differential equation ; Mathematics ; Engineering ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Haozhe Hu | Yongquan Jiang | Yan Yang | Jim X. Chen
Source:  (article, 2023-10-21)
Field: Materials Science — Materials Chemistry

Enhanced Template-Free Reaction Prediction with Molecular Graphs and Sequence-based Data Augmentation Retrosynthesis and forward synthesis prediction are fundamental challenges in organic synthesis, computer-aided synthesis planning (CASP), and computer-aided drug design (CADD). The objective is to predict plausible reactants for a given target product and its corresponding inverse task. With the rapid development of deep learning, numerous approaches have been proposed to solve this problem from various perspectives. The methods based on molecular graphs benefit from their rich features embedded inside but face difficulties in applying existing sequence-based data augmentations due to the permutation invariance of graph structures. In this work, we propose SeqAGraph, a template-free approach that annotates input graphs with its root atom index to ensure compatibility with sequence-based data augmentation. The matrix product for global attention in graph encoders is implemented by indexing, elementwise product, and aggregation to fuse global attention with local message passing without graph padding. Experiments demonstrate that SeqAGraph fully benefits from molecular graphs and sequence-based data augmentation and achieves state-of-the-art accuracy in template-free approaches. Computer science ; Autoencoder ; Graph ; Theoretical computer science ; Search engine indexing ; Product design ; Artificial intelligence ; Data mining ; Product (mathematics) ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yilong Zhou | Sigbjorn Bore | Andrea R. Tao | Francesco Paesani | Gaurav Arya
Source: ChemRxiv (preprint, 2023-04-04)
Field: Materials Science — Materials Chemistry

Many-body Potential for Simulating the Self-Assembly of Polymer-Grafted Nanoparticles in a Polymer Matrix Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many- body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine learning (ML) approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from potential of mean force (PMF) calculations. This potential developed here reduces the computational cost by several orders of magnitude, thereby allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases, such as 1D strings and 2D hexagonal sheets, which cannot be achieved using two-body potentials, but can also help discover interesting new phases, such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many- body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms in the overall potential in the observed behavior. Polymer ; Nanoparticle ; Nanotechnology ; Materials science ; Chemical physics ; Statistical physics ; Potential of mean force ; Matrix (chemical analysis) ; Biological system ; Computer science ; Physics ; Molecular dynamics Material Dynamics and Properties ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Yulun Wu | Yumeng Li
Source:  (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Physics-Informed Multitask Learning for Material Development Abstract Accurately predicting the materials’ responses, such as strain and energy, under certain loading conditions is crucial for developing fundamental structure-property relationships and facilitating material design. However, this process can be computationally expensive and challenging, especially for heterogeneous material systems with a large design space, where physics-based repetitive numerical simulations may be required. Furthermore, conducting physical experiments over such a large design space can be both time-consuming and costly. To address these challenges, convolutional neural networks (CNNs) have become increasingly popular as a computationally feasible way to make high-fidelity predictions for various materials, based on simulation results or experimental data. CNNs are particularly useful for materials with complex microstructures that are difficult to characterize or quantify, especially when suitable descriptors are not available. However, these models often suffer from poor transferability and reduced robustness due to limited training data. To overcome this challenge, we propose using multi-task learning (MTL) to provide deep learning models with more knowledge of material behaviors. MTL can improve the performance of multiple related learning tasks by leveraging useful information among them. In this paper, we explore a novel MTL model to improve the fidelity of predictions for both displacement and strain energy. We verify the ability of our proposed model by using the Material MNIST dataset as a case study, where one task is to predict strain energy based on different material microstructures. We demonstrate that, through MTL, the CNN model can achieve a better understanding of materials’ responses and achieve more robust performance in predicting responses for previously unseen material structures. Computer science ; MNIST database ; Robustness (evolution) ; Machine learning ; Artificial intelligence ; Fidelity ; Convolutional neural network ; Deep learning ; High fidelity ; Multi-task learning ; Bridging (networking) ; Task (project management) ; Systems engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: B. P. Harsha | Amar Patnaik | M. K. Banerjee | Ernst Kozeschnik
Source: International Journal of Metalcasting (article, 2023-09-23)
Field: Materials Science — Materials Chemistry

Triboinformatics Modeling of Dry Sliding Wear of High Manganese Hadfield Steel alloys Materials science ; Metallurgy ; Austenite ; Alloy ; Ultimate tensile strength ; Manganese ; Microstructure ; Tribology ; Hardening (computing) ; Annealing (glass) ; Composite material Metal Alloys Wear and Properties ; Mechanical stress and fatigue analysis ; Metal and Thin Film Mechanics
Authors: Mallikarjuna Rao Gundavarapu | Mandapati Bhavita | Mellacheruvu Sahithi | N.A. Varsha | Rakesh Kumar | Y. Lakshmi Prasanna
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

IoT-Powered Intelligent Framework for Detecting Food Adulteration: A Smart Approach Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time-consuming, labor-intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration.Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for preprocessing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real-time, data- driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system. Computer science ; Food safety ; Internet of Things ; Quality (philosophy) ; Preprocessor ; Food quality ; Data pre-processing ; Machine learning ; Artificial intelligence ; Risk analysis (engineering) ; Embedded system ; Food science ; Business Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Tianyao Wang | Jianfeng Li | Hongdong Zhang | Jeff Z. Y. Chen
Source: Chinese Journal of Polymer Science (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

Designs to Improve Capability of Neural Networks to Make Structural Predictions Computer science ; Representation (politics) ; Artificial neural network ; STRIDE ; Artificial intelligence ; Position (finance) ; Mechanism (biology) ; Task (project management) ; Field (mathematics) ; Lattice (music) ; Theoretical computer science ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Farook Mustafa | Ayman El‐Hag
Source: IET conference proceedings. (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Hydrophobicity classification of RTV silicone rubber-coated insulators using deep learning algorithms Outdoor polymeric insulators based on silicone rubber are widely used in power transmission and distribution networks. They exhibit several advantages like light weight, vandalism resistance and superior pollution performance due to their hydrophobic surface property. However, silicone rubber insulators suffer from aging that will lead to the loss of their hydrophobicity property. Various methods have been used to classify the hydrophobicity of the insulator surface including static and dynamic contact angle measurement and hydrophobicity class as per the IEC 62073 standard. The main goal of this paper is to use deep learning techniques to automatically assess the hydrophobicity classes (as per the IEC 62073 standards) of non-ceramic insulators under various conditions. A dataset of the hydrophobicity classes (HC1-HC6) was created including 4197 images each having 224×224 pixels size to train the proposed model. Convolutional Neural Networks (CNN) and Transfer Learning (TL) were used in this study to categorize and evaluate the hydrophobicity classes of ceramic insulators coated with room-temperature vulcanized silicone rubber. Compared to other CNN pre-trained models, the MobileNet model was found to have the highest accuracy and the lowest training time under several conditions, with maximum classification accuracy of 97.8%. Silicone rubber ; Materials science ; Natural rubber ; Computer science ; Silicone ; Algorithm ; Composite material High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring
Authors: Amir Mohammad Mirzaei
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Materials Science — Polymers and Plastics

Stress, Strain, or Energy: Which One Is the Superior Parameter to Estimate Fatigue Life of Notched Components? An Answer by a Novel Machine Learning-Based Framework This paper introduces a simple framework for accurately predicting the fatigue lifetime of notched components by employing various machine learning algorithms applied to a wide range of materials, loading conditions, notch geometries, and fatigue lives. Traditional approaches for this task have relied on empirical relationships involving one of the mechanical properties, such as stress, strain, or energy. This study goes further by exploring which mechanical property serves as a better measure. The key idea of the framework is to use the gradient of the mechanical properties (stress, strain, and energy) to distinguish between different notch geometries. To demonstrate the accuracy and broad applicability of the framework, it is initially validated using isotropic materials, subsequently applied to samples produced through additive manufacturing techniques, and ultimately tested on carbon fiber laminated composites. The research demonstrates that the gradient of all three measures can be effectively employed to estimate fatigue lifetime, with stress-based predictions exhibiting the highest accuracy. Among the machine learning algorithms investigated, Gradient Boosting and Random Forest yield the most successful results. A noteworthy finding is the significant improvement in prediction accuracy achieved by incorporating new data generated based on the Basquin equation. Isotropy ; Range (aeronautics) ; Energy (signal processing) ; Materials science ; Vibration fatigue ; Computer science ; Measure (data warehouse) ; Random forest ; Stress (linguistics) ; Gradient boosting ; Structural engineering ; Mechanical engineering ; Machine learning ; Composite material ; Mathematics ; Fatigue testing ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Mechanical and Thermal Properties Analysis
Authors: Yanxing Wang | Théo Jaffrelot Inizan | Chengwen Liu | Jean‐Philip Piquemal | Pengyu Ren
Source: ChemRxiv (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

Incorporating Neural Networks into the AMOEBA Polarizable Force Field Neural network potentials (NNPs) have great potential to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. However, most of the NNPs remain restricted by the locality assumption that ensures the model's transferability and scalability but misses out the long-range interactions. Here we present an integrated non-reactive hybrid model, AMOEBA+NN, which employs the AMOEBA potential for the short- and long-range non-bonded interactions and an NNP to capture the remaining local (covalent) contributions. A first AMOEBA+NN model was trained on the conformational energy of ANI-1x dataset and tested on several external datasets ranging from small molecules to tetrapeptides. It was encouraging to see that the hybrid model becomes significantly advantageous over the baseline models in term of accuracy as the molecules get larger, offering perspectives for the development of a generalized and improved approach. Locality ; Transferability ; Scalability ; Force field (fiction) ; Ranging ; Range (aeronautics) ; Computer science ; Artificial neural network ; Amoeba (genus) ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Engineering ; Biology ; Aerospace engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods