jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Madeleine Darbyshire | Shaun Coutts | Eleanor Hammond | Fazilet Gokbudak | Cengiz Öztireli | Petra Bosilj | Junfeng Gao | Elizabeth Sklar | Simon Parsons
Source: arXiv (Cornell University) (preprint, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops As the burden of herbicide resistance grows and the environmental costs of excessive herbicide use become clear, new approaches to managing weed populations are needed. This is particularly true for cereal crops, like wheat and barley, that are staple foods and occupy a globally significant share of farmland. Even modest advances in weed management practices across these crops could deliver major benefits for both the environment and food security. Blackgrass is a major grass weed which causes particular problems in cereal crops in north-west Europe, a major cereal production area, because it has high levels of herbicide resistance. Detecting blackgrass is also difficult due to its similarity to cereals. Yet, a systematic review of the literature on weed recognition in wheat and barley, included in this study, highlights that blackgrass - and grass weeds more broadly - have received less research attention compared to certain broadleaf weeds. With the use of machine vision and multispectral imaging, we investigate the effectiveness of state-of-the-art methods to identify blackgrass in wheat and barley crops. As part of this work, we present the Eastern England Blackgrass Dataset, a large dataset with which we evaluate several key aspects of blackgrass weed recognition. Firstly, we determine the performance of different CNN and transformer-based architectures on images from unseen fields. Secondly, we demonstrate the role that different spectral bands have on the performance of weed classification. Lastly, we evaluate the role of dataset size in classification performance for each of the models trialled. All models tested achieved an accuracy greater than 80%. Our best model achieved 89.6% and that only half the training data was required to achieve this performance. Our dataset is available at: https://lcas.lincoln.ac.uk/wp/research/data-sets-software/eastern-england-blackgrass-dataset . Multispectral image ; Agronomy ; Environmental science ; Biology ; Geography ; Remote sensing Spectroscopy and Chemometric Analyses
Authors: Ifeanyi Uchegbulam | S. C. Agbo | Onyinyechi Pauline Elenwo | Emmanuel Owoichoechi Momoh
Source: Research on Engineering Structures and Materials (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Flux potentials of local talc in low voltage electro-porcelain insulator production Increasing demand for accessories like porcelain electric insulators have continued in developing countries causing overdependence on imported products. Meanwhile, raw materials for their local production are available in commercial quantities with high firing temperature being the major challenge. Hence, the need for fluxing agents that can reduce the working temperature led to the investigation of Talc as a partial replacement of conventional Feldspar. An optimum replacement of 10% Feldspar with Talc was adopted, and this recipe showed microstructurally enriched glassy phase, primary and secondary Mullite with other crystalline phases like Cristobalite, Microcline and Albite at 1200oC with micropores evident in both the Talc and Feldspar formulations. The produced insulator fired at 1200oC offered an optimum thermal conductivity of 0.2768W/mK with reductions in water absorption, apparent porosity and linear shrinkage by 78.29%, 76.02% and 19.75% respectively with a 9.48% rise in bulk density. The electrical performance comprised Inception, Withstand and Breakdown Voltages of 10.2 kV, 14 kV and 20 kV respectively at leakage currents of 0.2 mA, 1.2 mA and 2 mA accordingly. With a withstand voltage above 11 kV and breakdown voltage below 22 kV, this pin-type porcelain insulator will find wide application in both electric substations and end user load points like commercial and residential electric power lines. The novelty lies on the fluxing and filler potentials of Talc as a locally-sourced mineral in porcelain insulator production which increases the market value of Nigerian Talc while reducing importation, costs of energy and raw materials in porcelain production. Talc ; Flux (metallurgy) ; Insulator (electricity) ; Voltage ; Materials science ; Condensed matter physics ; Composite material ; Electrical engineering ; Physics ; Metallurgy ; Engineering High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission
Authors: Sergio Martínez-Agüero | Antonio G. Marqués | Inmaculada Mora-Jiménez | Joaquín Álvarez-Rodríguez | Cristina Soguero-Ruiza
Source: arXiv (Cornell University) (preprint, 2024-02-09)
Field: Chemistry — Spectroscopy

Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series Electronic health records (EHR) is an inherently multimodal register of the patient's health status characterized by static data and multivariate time series (MTS). While MTS are a valuable tool for clinical prediction, their fusion with other data modalities can possibly result in more thorough insights and more accurate results. Deep neural networks (DNNs) have emerged as fundamental tools for identifying and defining underlying patterns in the healthcare domain. However, fundamental improvements in interpretability are needed for DNN models to be widely used in the clinical setting. In this study, we present an approach built on a collection of interpretable multimodal data-driven models that may anticipate and understand the emergence of antimicrobial multidrug resistance (AMR) germs in the intensive care unit (ICU) of the University Hospital of Fuenlabrada (Madrid, Spain). The profile and initial health status of the patient are modeled using static variables, while the evolution of the patient's health status during the ICU stay is modeled using several MTS, including mechanical ventilation and antibiotics intake. The multimodal DNNs models proposed in this paper include interpretable principles in addition to being effective at predicting AMR and providing an explainable prediction support system for AMR in the ICU. Furthermore, our proposed methodology based on multimodal models and interpretability schemes can be leveraged in additional clinical problems dealing with EHR data, broadening the impact and applicability of our results. Multivariate statistics ; Multiple drug resistance ; Series (stratigraphy) ; Computer science ; Time series ; Antimicrobial ; Artificial intelligence ; Statistics ; Data mining ; Machine learning ; Drug resistance ; Mathematics ; Biology ; Microbiology Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Bacterial Identification and Susceptibility Testing
Authors: Mengjia Xue | Siyi Huang | Wenting Xu | Tianwu Xie
Source: Frontiers in Plant Science (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging This research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species. Trait ; Cultivar ; Biology ; Phenotype ; Artificial intelligence ; Botany ; Evolutionary biology ; Computer science ; Genetics Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Isotope Analysis in Ecology
Authors: Zeshan Ali | Zulkarnain Abbas | Saqlain Abbas
Source: Advanced Materials & Sustainable Manufacturing (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigation of High Vibration Phenomena in Steam Turbine: An Experimental Exploration into Root Causes and Material Analysis The steam turbine is a rotating device subject to axial and radial shaft shifts that can induce vibrations during operation. Tools such as monitoring systems and proximity probe sensors are essential to monitoring these vibrations. High vibrations affect the machine’s performance, increasing the risk of malfunctions and reducing its lifespan, and also pose risks to operational and maintenance personnel. The intensified vibrations in the bearing pedestals signify the underlying issues with the machine’s normal operation. Consequently, problems such as rotor imbalance, coupling misalignment, mechanical looseness, material failure, and bent shaft may be caused. In the current study, the latest field-proven automatic diagnostic of rotary equipment (ADRE 408) data acquisition system is installed by Bentley Nevada to investigate the root cause of high vibration. This advanced diagnostic system facilitates a comprehensive assessment, enabling us to effectively identify and address underlying problems. Hence, the current research includes a thorough diagnosis of the underlying problems to attenuate the risks of high vibrations in the steam turbine, coupled with strategic maintenance planning and corrective actions. Steam turbine ; Vibration ; Root (linguistics) ; Root cause analysis ; Turbine ; Structural engineering ; Materials science ; Engineering ; Environmental science ; Forensic engineering ; Mechanical engineering ; Acoustics ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Machine Fault Diagnosis Techniques
Authors: Ellen Oudkerk-Sodia
Source: Microscopy Today (article, 2024-02-22)
Field: Materials Science — Biomaterials

Determining the Young’s Moduli of Collagen Fibrils with the Winkler Foundation Model Abstract This report highlights the Winkler Foundation Model, a novel method to determine the Young’s moduli of a set of dehydrated and aged collagen fibrils. Atomic force microscopy (AFM) was used to image the surface of collagen fibrils and to perform force curves. The data were analyzed with the Gwyddion software to obtain fibril diameters and force curve slopes. These quantitative outputs were substituted into the analytical Winkler equation to obtain a final value for the Young’s moduli of the collagen fibrils. The investigation found Young’s moduli values within the giga Pascal (GPa) range, which is consistent with previous experiments on dehydrated collagen fibrils. Hence, this investigation suggests that the Winkler Foundation Model may be a candidate to determine the Young’s modulus of collagen fibrils. This may result in significant applications in cancer research and tissue engineering. Foundation (evidence) ; Moduli ; Materials science ; Collagen fibril ; Structural engineering ; Engineering ; Biophysics ; Physics ; Biology ; Geography ; Archaeology Collagen: Extraction and Characterization ; Textile materials and evaluations
Authors: Zhigang Ding | Jingjing Jiang | Jishi Zheng | Linghua Kong
Source: Electronics (article, 2024-08-27)
Field: Chemistry — Analytical Chemistry

Machine Vision-Based Method for Reconstructing the Vehicle Coordinate System in End-of-Line ADAS Calibration To ensure the accuracy and reliability of Advanced Driver Assistance Systems (ADAS), it is essential to perform offline calibration before the vehicles leave the factory. This paper proposes a method for reconstructing the vehicle coordinate system based on machine vision, which can be applied to the offline calibration of ADAS. Firstly, this study explains the preliminary preparations, such as the selection of feature points and the choice of camera model, combining actual application scenarios and testing requirements. Subsequently, the YOLO model is trained to identify and obtain feature regions, and feature point coordinates are extracted from these regions using template matching and ellipse fitting. Finally, a validation experiment is designed to evaluate the accuracy of this method using metrics such as the vehicle’s lateral and longitudinal offset distances and yaw angle. Experimental results show that, compared to traditional vehicle alignment platforms, this method improves reconstruction accuracy while reducing costs. Coordinate system ; Calibration ; Computer vision ; Artificial intelligence ; Line (geometry) ; Computer science ; Machine vision ; Engineering ; Mathematics ; Geometry Spectroscopy and Chemometric Analyses ; Advanced Measurement and Detection Methods ; Industrial Vision Systems and Defect Detection
Authors: Matluck Afolabi | Henry Chukwuemeka | Olisakwe | Thompson Odion Igunma
Source: Global journal of research in multidisciplinary studies (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Catalysis 4.0: A framework for integrating machine learning and material science in catalyst developmentpna This paper introduces Catalysis 4.0, a comprehensive framework for leveraging artificial intelligence (AI) and machine learning (ML) in the development of catalytic materials. The framework is structured around three key components: data-driven material discovery, virtual testing environments, and adaptive feedback loops. Data-driven material discovery utilizes AI algorithms to predict catalytic performance based on extensive material properties and high-quality datasets. Virtual testing environments provide simulation platforms to evaluate catalyst efficiency under various industrial conditions. This significantly reduces traditional experimentation time and costs. Adaptive feedback loops integrate real-time industrial data, enabling continuous refinement and improvement of AI models. This approach accelerates the catalyst development cycle, enhancing efficiency and reliability. The paper discusses the potential impact of this framework on industries such as refining and pharmaceutical manufacturing, where AI-driven catalyst design can enhance performance and reduce costs. Future directions for research include improving data collection techniques, developing sophisticated AI algorithms, and fostering interdisciplinary collaboration. The framework's implementation can revolutionize catalyst development, driving innovation and sustainability in critical industrial sectors. Catalysis ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Ayesha Ahmad
Source: Research Journal of Pharmacy and Technology (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Chemical Reaction Prediction using Machine Learning A significant revolution in organic chemistry is being driven by artificial intelligence. A number of platforms, including applications for planned synthesis and reaction prediction Machine learning has successfully integrated itself into the daily work of organic chemists, enabling in synthetic issues with a specific domain. Contrary to retrosynthetic models and reaction prediction, the Despite the huge potential of response yield prediction, it has gotten less attention. accurately forecasting the rates of response conversion. Reaction generates models that specify the proportion. Chemists to choose high produced reactions and score synthesis methods, decreasing the number of tries. The reactants transformed to the wanted products. For high-capacity studies, yield estimates have primarily been made using a firm encoding of reactants, focused molecular patterns, or calculated chemicals used to describe. Machine learning ; Computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Masahiko Okumura
Source:  (article, 2024-06-16)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Simulations: An Application of Machine Learning for Material Science Machine learning (ML) has made remarkable progress in recent years, and materials informatics has advanced in materials science. On the other hand, atomic-scale simulation is still necessary to understand the details of micro-scale physics and chemistry, and its development is still needed. In atomic-scale dynamics simulations, classical molecular dynamics (CMD) based on classical mechanics and ab initio molecular dynamics (AIMD) based on quantum mechanics are mainly used. However, neither of these methods is ideal (low computational cost and high accuracy), because the former has low computational cost but low accuracy and the latter has high accuracy but high computational cost. Recently, a new method called “machine learning molecular dynamics (MLMD)” using ML has been proposed [1]. This method uses an artificial neural network to learn the results of first-principles calculations (quantum mechanics calculations). It achieves accuracy comparable to AIMD at a low computational cost, approaching that of CMD. However, because this method is new, its application limits are unclear, especially for materials with complex atomic configurations or those containing many atomic species, which is the frontier of this method. Computer science ; Molecular dynamics ; Dynamics (music) ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science
Authors: Radu A. Talmazan | Jakob Gamper | Iván Castillo | Thomas S. Hofer | Maren Podewitz
Source: ChemRxiv (preprint, 2024-06-24)
Field: Materials Science — Materials Chemistry

From Data to Chemistry: Revealing Causality and Reaction Coordinates through Interpretable Machine Learning in Supramolecular Transition Metal Catalysis Supramolecular transition metal catalysts with tailored reaction environments allow for the usage of abundant 3d metals as catalytic centres, leading to more sustainable chemical processes. However, such catalysts are large and flexible systems with intricate interactions, resulting in complex reaction coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, leveraging quantum mechanics/molecular mechanics (QM/MM) molecular dynamics in explicit solvent, to investigate a Cu(I)-calix[8]arene catalysed C-N coupling reaction. The system complexity and high amount of data generated from sampling the reaction require automated analyses. To identify and quantify the reaction coordinate from noisy simulation trajectories, we applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) in a consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). Leveraging a Granger Causality model, we go beyond the traditional view of a reaction coordinate, by defining it as a sequence of molecular motions that led up to the reaction. Causality (physics) ; Supramolecular chemistry ; Catalysis ; Transition metal ; Chemistry ; Transition (genetics) ; Computer science ; Organic chemistry ; Physics ; Molecule ; Biochemistry ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling
Authors: Takuya Seki | Yudai Shinozaki | Ryosuke Ito | Shunsuke Sato | Jun Takeya | Toshihiro Okamoto | Go Watanabe
Source: ChemRxiv (preprint, 2024-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Determining Molecular Packing of Organic Semiconductors: Toward Accurate Crystal Structure Prediction Organic semiconductors (OSCs) with π-electron skeletons (π-cores) have garnered significant attention. The development of innovative molecules with high carrier mobility necessitates strategic molecular design. One critical property affecting the carrier mobility of π-conjugated OSCs is the molecular arrangement, particularly the two-dimensional (2D) molecular packing of the π-cores, such as π-stacking, herringbone (HB) packing, and brickwork (BW) packing. These molecular packing structures, which are similar 2D packings, have not been theoretically predicted, leading chemists to design new OSC molecules based on empirical knowledge. Therefore, computational science and informatics are crucial for the strategic design of OSC molecules with unprecedented properties and functions. In this study, we introduce a machine learning method to determine with high accuracy whether an OSC forms the HB packing, a 2D molecular packing known to enhance carrier mobility. We also present a computational method to predict the crystal structure of an OSC from its chemical structure using molecular mechanics (MM) calculations and molecular dynamics (MD) simulations, coupled with our proposed machine learning model to classify the type of 2D molecular packing. Organic semiconductor ; Semiconductor ; Materials science ; Crystal (programming language) ; Crystal structure prediction ; Artificial intelligence ; Computer science ; Machine learning ; Crystal structure ; Pattern recognition (psychology) ; Crystallography ; Optoelectronics ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Jihoon Chung | Justin Li | Amirul Islam Saimon | Pengyu Hong | Zhenyu Kong
Source: Scientific Reports (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

Predicting the stereoselectivity of chemical reactions by composite machine learning method Abstract Stereoselective reactions have played a vital role in the emergence of life, evolution, human biology, and medicine. However, for a long time, most industrial and academic efforts followed a trial-and-error approach for asymmetric synthesis in stereoselective reactions. In addition, most previous studies have been qualitatively focused on the influence of steric and electronic effects on stereoselective reactions. Therefore, quantitatively understanding the stereoselectivity of a given chemical reaction is extremely difficult. As proof of principle, this paper develops a novel composite machine learning method for quantitatively predicting the enantioselectivity representing the degree to which one enantiomer is preferentially produced from the reactions. Specifically, machine learning methods that are widely used in data analytics, including Random Forest, Support Vector Regression, and LASSO, are utilized. In addition, the Bayesian optimization and permutation importance tests are provided for an in-depth understanding of reactions and accurate prediction. Finally, the proposed composite method approximates the key features of the available reactions by using Gaussian mixture models, which provide suitable machine learning methods for new reactions. The case studies using the real stereoselective reactions show that the proposed method is effective and provides a solid foundation for further application to other chemical reactions. Stereoselectivity ; Machine learning ; Artificial intelligence ; Computer science ; Steric effects ; Chemical reaction ; Bayesian probability ; Random forest ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography