jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Halil Mertkan Sahin | Tajul Miftahushudur | Bruce Grieve | Hujun Yin
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Segmentation of Weeds and Crops Using Multispectral Imaging and Crf-Enhanced U-Net Multispectral image ; Segmentation ; Agronomy ; Artificial intelligence ; Geography ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yeonsu Kim | Yoon A. Kim | Seo Hwee Park | YunHo Kim
Source: Journal of Mechanical Science and Technology (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Hybrid numerical modeling of ballistic clay under low-speed impact using artificial neural network Finite element method ; Artificial neural network ; Indentation ; Structural engineering ; Boundary value problem ; Strain rate ; Boundary (topology) ; Plasticity ; Computer science ; Engineering ; Materials science ; Artificial intelligence ; Mathematics ; Composite material ; Mathematical analysis High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Optical measurement and interference techniques
Authors: Isaac Wilfried Sanou | Julien Baderot | Yannick Benezeth | Stéphanie Bricq | Franck Marzani | Sergio Martinez | J. Foucher
Source:  (preprint, 2023-07-28)
Field: Materials Science — Surfaces, Coatings and Films

Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images For semiconductor applications, billions of objects are manufactured for a single device such as a central processing unit (CPU), storage drive, or graphical processing unit (GPU). To obtain functional devices, each element of the device has to follow precise dimensional and physical specifications at the nanoscale. Generally, the pipeline consists to annotate an object in an image and then take the measurements of the object. Manually annotating images is extremely time-consuming. In this paper, we propose a robust and fast semi-automatic method to annotate an object in a microscopy image. The approach is a deep learning contour-based method able first to detect the object and after finding the contour thanks to a constraint loss function. This constraint follows the physical meaning of electron microscopy images. It improves the quality of boundary detail of the vertices of each object by matching the predicted vertices and most likely the contour. The loss is computed during training for each object using a proximal way of our dataset. The approach was tested on 3 different types of datasets. The experiments showed that our approaches can achieve state-of-the-art performance on several microscopy images dataset. Computer science ; Artificial intelligence ; Pipeline (software) ; Object (grammar) ; Computer vision ; Automation ; Microscopy ; Lithography ; Constraint (computer-aided design) ; Pattern recognition (psychology) ; Optics ; Physics ; Mathematics ; Engineering Electron and X-Ray Spectroscopy Techniques ; Image Processing Techniques and Applications ; Machine Learning in Materials Science
Authors: Ahmed K. Khamis | Mohammed Agamy
Source:  (article, 2023-03-19)
Field: Materials Science — Materials Chemistry

Circuit Structure Dependent Multi-Variable Regression Model Based Predictions for DC-DC Converters The new regression model based on converter circuit structure is introduced, allowing for predictions on converter circuit level and internal parameter predictions. The proposed model is applicable to any converter circuit regardless of the number of components or connections present in the circuit. This is achieved by properly mapping all physical and switching circuit properties whether the converter circuits working in continuous conduction mode (CCM) to graph representation, so that machine learning (ML) algorithms and applications can be easily applied. This is useful in applications such as AI based circuit synthesis, automatic circuit layout generation, AI circuit design automation and many others. Three of the most common converters (Buck, Boost, and Buck-boost) are used as example circuit applied to model and the target is to predict the gain and current ripples in inductor. The model achieves 99.88% on the <tex>$R^{2}$</tex> measure and a mean square error (MSE) of 0.023. Computer science ; Converters ; Electronic circuit ; Equivalent circuit ; Inductor ; Representation (politics) ; Buck converter ; Network analysis ; Electronic engineering ; Circuit design ; Control theory (sociology) ; Voltage ; Engineering ; Electrical engineering ; Artificial intelligence Machine Learning in Materials Science ; Silicon Carbide Semiconductor Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: William R. Johnson | Xian-Kui Zhu | Robert Sindelar | Bruce Wiersma
Source:  (article, 2023-07-16)
Field: Materials Science — Metals and Alloys

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels Abstract The common use of pressure vessels (PVs) and pipelines and the high cost of their failure or overdesign in the oil and gas industry makes predicting their burst pressure critical. However, experimental tests of burst pressure are expensive and finite element analysis (FEA) is time consuming. Artificial neural networks (ANNs) hold the promise of rapid prediction of burst pressure for a wide range of PV materials and geometries, including pipelines with defects such as corrosion. This paper demonstrates ANNs designed and trained to accurately predict the burst pressure of both thick and thin-walled PVs for various strain hardening carbon steels. To accomplish this, we trained single and double hidden layer ANNs on experimental data, augmented with FEA, data of predicted burst pressure to create a larger database. A statistical study of hundreds of models exploring the key hyperparameters provided statistically optimal hyperparameters, resulting in highly accurate ANNs. The results, two ANNs with comparably low prediction error across geometries ranging from 3 &amp;lt; Do/t &amp;lt; 120, demonstrate the first use of ANNs to address both thick and thin-walled PV burst pressure within a single network. This is an important step towards designing ANNs for predicting the burst pressure of PVs and pipelines with defects. Artificial neural network ; Pipeline transport ; Hyperparameter ; Finite element method ; Materials science ; Computer science ; Structural engineering ; Artificial intelligence ; Engineering ; Mechanical engineering Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Structural Integrity and Reliability Analysis
Authors: Muhammad Sohail | Zunaira Urooj | Xinlan Bian | Sobia Noreen | Mirza Muhammad Faran Ashraf Baig | Jannat BiBi | Xing Zhang | Bingzhi Li | He Huang
Source: TrAC Trends in Analytical Chemistry (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Feasibility of MoS2 for analytical and bioanalytical applications Bioanalysis ; Nanotechnology ; Molybdenum disulfide ; Graphene ; Materials science ; Biocompatibility ; Surface modification ; Monolayer ; Absorbance ; Computer science ; Chemistry ; Engineering ; Chemical engineering 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques ; MXene and MAX Phase Materials
Authors: Wenzhi Yao | Yanyan Qu | Miao Zhou | Wentao Wang | Aodi Zhang | Zhenzhen Feng | Hongxia Yan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Increasing the Bifunctional Oer/Orr Activity of 3d Transition Metals Doped G-C3n3 by Controlling the Charge States Bifunctional ; Doping ; Charge (physics) ; Transition metal ; Transition (genetics) ; Materials science ; Chemistry ; Nanotechnology ; Optoelectronics ; Physics ; Catalysis ; Quantum mechanics Inorganic Chemistry and Materials
Authors: Marc Lehner | Paul Katzberger | Niels Maeder | Carl C. G. Schiebroek | Jakob Teetz | Gregory A. Landrum | Sereina Riniker
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Materials Science — Materials Chemistry

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source / open data from public repositories. Dash ; Partial charge ; Workflow ; Computer science ; Substructure ; Hierarchy ; Data mining ; Theoretical computer science ; Charge (physics) ; Physics ; Engineering ; Database ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Diana Hodyna | Vasyl Kovalishyn | Yanina Romanenko | Ivan Semenyuta | Volodymyr Blagodatny | Maryna Kachaeva | Oleksandr Brazhko | Larysa Metelytsia
Source: Chemistry & Biodiversity (article, 2023-08-08)
Field: Chemistry — Organic Chemistry

Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from -8.4 to -9.4 kcal/mol. Quinoline ; Chemistry ; Hydrazone ; Multiple drug resistance ; Antibiotics ; Combinatorial chemistry ; Pharmacology ; Stereochemistry ; Biochemistry ; Organic chemistry ; Biology Synthesis and biological activity ; Computational Drug Discovery Methods ; Bioactive Compounds and Antitumor Agents
Authors: Salil Bavdekar | Richard G. Hennig | Ghatu Subhash
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Augmenting the discovery of computationally complex ceramics for extreme environments with machine learning Property (philosophy) ; Convex hull ; Space (punctuation) ; Throughput ; Materials science ; Density functional theory ; Stoichiometry ; Computer science ; Ceramic ; Energy (signal processing) ; Regular polygon ; Machine learning ; Computational chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced ceramic materials synthesis
Authors: Sohel Siraj | Naveen Bokka | Anurag Gade | Sarang Akella | Chandra Sekhar Reddy Kolli | Parikshit Sahatiya
Source: IEEE Journal on Flexible Electronics (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Development of Flexible ReS<sub>2</sub>/MXene Based Electromechanical Sensor for Deep Learning Assisted Temporal Dependent Alphabet Pattern Recognition This article demonstrates the fabrication of a flexible electromechanical sensor (pressure and strain) utilizing ReS2 as active material and patterned Ti3C2Tx metal carbide/nitride (MXene) as the contact for the amplitude- and time-dependent alphabet recognition pattern concerning high-security applications. The sensitivity of the pressure sensor is 0.139 kPa−1, and the gauge factor of the fabricated strain sensor is 0.436. The physical explanation for both pressure and strain stimuli is explained in terms of modulation of the Schottky barrier height of ReS2/MXene by extracting the band diagram using ultraviolet photoelectron spectroscopy (UPS) measurements. To demonstrate the real-time application of the fabricated device, characterization of each of the 26 English alphabets (individual) is performed by obtaining their current–time data, which also collects the speed and magnitude of the writing. A detailed architecture of the convolutional neural network is presented with an accuracy of 96.20%. The successful demonstration of a low-cost flexible electromechanical sensor for touch-pad applications finds numerous applications in security, signature forging, and medical devices. Alphabet ; Artificial intelligence ; Strain gauge ; Computer science ; Electrical engineering ; Engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Go Yamamoto | Yu Chen | Akira Kunitomo | Nozomu Shigemitsu | Tatsunori Shindo
Source: Carbon Trends (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Decreasing vacancy-defect sensitivity in multi-walled carbon nanotubes through interwall coupling Nanostructures of multi-walled carbon nanotubes (MWCNTs) that efficiently suppress the strength reduction caused by a string of three-atom vacancy defect were investigated by machine-learning-assisted molecular dynamics simulation. Under the calculation conditions, five-walled nanostructures in which the chirality of the innermost tube was (8,8) and the crosslink densities of 0.7%, 1.3%, 0.4%, and 0.1% (from outside to inside) were determined to be optimal, with the nominal tensile strength and Young's modulus reaching 66–69 GPa and 715–725 GPa, respectively. The fracture process varied depending on the spatial position relationship between the crosslinks and vacancy defects. In some MWCNT models with high nominal tensile strength, the crack tip opening displacement was effectively suppressed by the crosslink being located along the longitudinal line of the tube from the defect tip. The crosslink densities at each interwall also affected the tensile strength properties considerably, especially between the outermost wall and its adjacent walls. Materials science ; Vacancy defect ; Ultimate tensile strength ; Carbon nanotube ; Composite material ; Atom (system on chip) ; Nanostructure ; Molecular dynamics ; Modulus ; Displacement (psychology) ; Coupling (piping) ; Nanoscopic scale ; Nanotechnology ; Crystallography ; Computational chemistry ; Chemistry Carbon Nanotubes in Composites ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Zhantao Chen | Peng Cheng | Alexander N. Petsch | Sathya R. Chitturi | Alana Okullo | Sugata Chowdhury | Chun Hong Yoon | Joshua J. Turner
Source: Machine Learning Science and Technology (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Bayesian experimental design and parameter estimation for ultrafast spin dynamics Abstract Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently uncover key quantum spin fluctuation parameters from x-ray photon fluctuation spectroscopy (XPFS) data. Our method is compatible with existing theoretical simulation pipelines and can also be used in combination with fast machine learning surrogate models in the event that real-time simulations are unfeasible. Our numerical benchmarks demonstrate the superior performance in predicting model parameters and in delivering more informative measurements within limited experimental time. Our method can be adapted to many different types of experiments beyond XPFS and spin fluctuation studies, facilitating more efficient data collection and accelerating scientific discoveries. Computer science ; Bayesian probability ; Spin (aerodynamics) ; Experimental data ; Statistical physics ; Event (particle physics) ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Thomas Head | Ryland T. Giebelhaus | Seo Lin Nam | A. Paulina de la Mata | James J. Harynuk | Paul R. Shipley
Source: ChemRxiv (preprint, 2023-08-13)
Field: Chemistry — Analytical Chemistry

Discriminating Extra Virgin Olive Oils from Common Edible Oils: Comparable Performance of PLS-DA Models Trained on Low-Field and High-Field 1H NMR Data Olive oil, the oil derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or mislabel cheaper oils to increase profitability. These other edible oils can have chemical profiles similar to extra virgin olive oil but can cause allergies in sensitive individuals. Given these consequences, there is a need for methods to rapidly authenticate olive oils. Nuclear magnetic resonance (NMR) has been used for this purpose, as it requires minimal sample preparation and is non-destructive. By utilizing NMR spectra of the samples and machine learning models trained on known olive oil and edible oils, oil samples can be classified and authenticated. While high-field NMRs are commonly used due to their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate, for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. Here, we compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop 1H NMR and high-field 400 MHz 1H NMR spectra. We demonstrated that PLS-DA models trained on low-field spectra perform comparably to those trained on high-field spectra. Olive oil ; Partial least squares regression ; Adulterant ; Olea ; Edible oil ; Chemometrics ; Oil field ; Proton NMR ; NMR spectra database ; Chemistry ; Pulp and paper industry ; Food science ; Mathematics ; Chromatography ; Spectral line ; Horticulture ; Statistics ; Physics ; Engineering ; Biology ; Organic chemistry ; Petroleum engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: S. Jaisankar | N. Vijayasharathi | M. Sivakumar | Ganesh Babu Loganathan | R. Manikandan | S. Sakthi
Source: Materials Today Proceedings (article, 2023-08-01)
Field: Materials Science — Polymers and Plastics

Prediction of tensile properties of MWCNT/Hemp fibre/fly ash reinforced epoxy polymer composites using various mathematical models Ultimate tensile strength ; Materials science ; Epoxy ; Composite material ; Response surface methodology ; Carbon nanotube ; Young's modulus ; Fly ash ; Central composite design ; Linear regression ; Design of experiments ; Modulus ; Mathematics ; Statistics Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Carbon Nanotubes in Composites
Authors: Xueqiang Fan | Xixi Gao | Yisen Deng | Bo Ma | Jingwen Liu | Zhaohua Zhang | Dingkai Zhang | Yuguang Yang | Cheng Wang | Bin He | Qiangqiang Nie | Zhidong Ye | Peng Liu | Jianyan Wen
Source: Frontiers in Physiology (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations <b>Objective:</b> This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). <b>Method:</b> Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. <b>Result:</b> In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. <b>Conclusion:</b> Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment. Metabolomics ; Metabolome ; KEGG ; Metabolism ; Metabolic pathway ; Fatty acid metabolism ; Lipid metabolism ; Amino acid ; Medicine ; Glutamine ; Biochemistry ; Biology ; Chemistry ; Internal medicine ; Bioinformatics ; Transcriptome ; Gene Advanced Nanomaterials in Catalysis ; Retinal Diseases and Treatments ; Intracranial Aneurysms: Treatment and Complications