jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilia Khomchenko | Andrei Fionov | Alexey Alekseev | D.V. Volkov | I. A. Semerikov | N. Kolachevsky | Aleksey K. Fedorov
Source: arXiv (Cornell University) (preprint, 2024-12-14)
Field: Chemistry — Spectroscopy

Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms Trapped ions are among the leading platforms for quantum technologies, particularly in the field of quantum computing. Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states. In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera. By transforming the images from the camera and applying conventional and quantum machine learning methods, including convolution, support vector machine (classical and quantum), and quantum annealing, we demonstrate a possibility to detect the positions and quantum states of ytterbium ions in a Paul trap. Quantum state detection is performed with an electron shelving technique: depending on the quantum state of the ion its fluorescence under the influence of a 369.5 nm laser beam is either suppressed or not. We estimate fidelities for conventional and quantum detection techniques. In particular, conventional algorithms for detecting $^{171}$Yb$^{+}$, such as the support vector machine and photon statistics-based method,as well as our quantum annealing-based approach, have achieved perfect fidelity, which is beneficial compared to standard techniques. This result may pave the way for ultrahigh-fidelity detection of trapped ions via conventional and quantum machine learning techniques. Trap (plumbing) ; Ion trap ; Ion ; Algorithm ; Quantum ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics Mass Spectrometry Techniques and Applications
Authors: Zonghao Liu
Source: Highlights in Science Engineering and Technology (article, 2024-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and development Analysis of Deep UV photodetectors The development of deep-ultraviolet (DUV) photodetectors has gained significant attention due to their broad applications. With the increasing demand for high-performance detectors that can operate in harsh environments, research on optimizing their materials and structural design has become a critical focus. This paper introduces the basic working principle, structural composition, and performance evaluation criteria of deep-ultraviolet (DUV) photodetectors, focusing on the analysis of the optimization of materials and structure to improve the performance of detectors. Deep ultraviolet photodetectors are based on the photovoltaic effect of semiconductor materials to realize the conversion of photoelectric signals. It is shown that the photo responsiveness and stability of the photodetector can be effectively improved by introducing a composite film of the rare earth element cerium tungstate (Ce-WO3). In addition, the structural optimization of graphene-β-Ga2O3 heterojunction and n-Ga2O3/p-GaN heterojunction is employed to significantly improve the spectral selectivity, responsivity and long-time stability of the detector. This paper also explores the potential of these improvements for applications in the fields of UV communications, UV optoelectronic integrated circuits, environmental monitoring and military spaceflight. Photodetector ; Optoelectronics ; Materials science ; Ultraviolet ; Detector ; Heterojunction ; Responsivity ; Photoelectric effect ; Semiconductor ; Photovoltaic system ; Optics ; Electrical engineering ; Physics ; Engineering Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Mitchell Rogers | Mihailo Azhar | Stefano Schenone | Simon F. Thrush | Bing Xue | Mengjie Zhang | Patrice Delmas
Source: Journal of the Royal Society of New Zealand (article, 2024-08-21)
Field: Chemistry — Analytical Chemistry

Evolving multispectral sensor configurations using genetic programming for estuary health monitoring Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images. Hyperspectral imaging ; Multispectral image ; Computer science ; Genetic programming ; Feature selection ; Feature extraction ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Genetic algorithm ; Support vector machine ; Feature (linguistics) ; Environmental science ; Machine learning ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Li Yin | Hong Tang | Tom Berlijn | Adrienn Ruzsinszky
Source: arXiv (Cornell University) (preprint, 2024-04-11)
Field: Materials Science — Materials Chemistry

A Pathway to Efficient Simulations of Charge Density Waves in Transition Metal Dichalcogenides: A Case Study for TiSe2 Charge density waves (CDWs) in transition metal dichalcogenides are the subject of growing scientific interest due to their rich interplay with exotic phases of matter and their potential technological applications. Here, using density functional theory with advanced meta-generalized gradient approximations (meta-GGAs) and linear response time-dependent density functional theory (TDDFT) with state-of-the-art exchange-correlation kernels, we investigate the electronic, vibrational, and optical properties in 1T-TiSe2 with and without CDW. In both bulk and monolayer TiSe2, the electronic bands and phonon dispersions in either normal (semi-metallic) or CDW (semiconducting) phase are described well via meta-GGAs, which separate the valence and conduction bands just as HSE06 does but with significantly more computational feasibility. Instead of the underestimated gap with standard exchange-correlation approximations and the overestimated gap with screened hybrid functional HSE06, the band gap of the monolayer TiSe2 CDW phase calculated by the meta-GGA MVS (151 meV) is consistent with the angle-resolved photoemission spectroscopy (ARPES) gap of 153 meV measured at 10 K. In addition, the gap of bulk TiSe2 CDW phase reaches 67 meV within the TASK approximation, close to the ARPES gap of 82 meV. Regarding excitations of many-body nature, for bulk TiSe2 in normal and CDW phases, the experimentally observed humps of electron energy loss spectroscopy and plasmon peak are successfully reproduced in TDDFT, without an obvious kernel dependence. To unleash the full scientific and technological potential of CDWs in transition metal dichalcogenides, the chemical doping, heterostructure engineering, and pump-probe techniques are needed. Our study opens the door to simulating these complexities in CDW compounds from first principles by revealing meta-GGAs as an accurate low-cost alternative to HSE06. Condensed matter physics ; Hybrid functional ; Density functional theory ; Angle-resolved photoemission spectroscopy ; Local-density approximation ; Charge density wave ; Time-dependent density functional theory ; Materials science ; Electronic structure ; Monolayer ; Band gap ; Photoemission spectroscopy ; Chemistry ; Physics ; Nanotechnology ; Spectral line ; Computational chemistry ; Quantum mechanics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties
Authors: Yoshitaka Umeno | Atsushi Kubo | Emi Kawai
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Inductive and Deductive Scale-Bridging In Hierarchical Multiscale Models for Dislocation Pattern Formation in Metal Fatigue Fatigue fracture accounts for a substantial fraction of failure cases in industrial products, especially in metal materials. While the mechanism of fatigue crack propagation can be understood in the mechanical point of view c... | Find, read and cite all the research you need on Tech Science Press Bridging (networking) ; Dislocation ; Materials science ; Scale (ratio) ; Computer science ; Physics ; Composite material Microstructure and mechanical properties ; Metallurgy and Material Forming ; Non-Destructive Testing Techniques
Authors: Sree Kala T. Suresh D.
Source: Journal of Electrical Systems (article, 2024-04-08)
Field: Chemistry — Analytical Chemistry

Exploring Spectral Patterns of Tomato Phytophthora Infections using the VMRNMAF Deep Learning Framework Identifying and mitigating diseases affecting tomato crops pose significant challenges for researchers and agricultural producers alike. Effective disease detection is crucial for optimizing yields and implementing targeted treatments. This study presents a comprehensive framework, Vector Machine Recurrent Neural with Manual Activation Function (VMRNMAF), for early prediction of Phytophthora infestans and other common tomato diseases. Leveraging image preprocessing techniques and feature identification processes, VMRNMAF accurately distinguishes between healthy and infected tomato segments. By integrating Recurrent Neural Networks (RNNs) and manual activation functions in image segmentation, this framework enhances disease identification accuracy. Throughout deep learning methodologies, VMRNMAF streamlines the identification of bacterial, viral infections, offering a promising solution for improving tomato crop management practices. Experimental results demonstrate the efficacy of VMRNMAF in accurately identifying diseased areas within tomato crops, underscoring its potential for enhancing agricultural sustainability and productivity. Phytophthora ; Biology ; Artificial intelligence ; Botany ; Computer science Spectroscopy and Chemometric Analyses
Authors: Е. А. Lakhina | N E Chernenko | N A Shandyba | S V Balakirev | M S Solodovnik
Source: Izvestiâ ÛFU. Tehničeskie nauki (article, 2024-12-28)
Field: Materials Science — Surfaces, Coatings and Films

INVESTIGATION OF THE INFLUENCE OF ANNEALING MODES OF THE GAAS(111) SURFACE ON THE CHARACTERISTICS OF NANOHOLES FORMED BY FOCUSED ION BEAMS AT VARIOUS EXPOSURE TIMES The popularity of container systems attracts the attention of many researchers in the field of informationtechnology. Containerization technology allows to reduce the cost of computing resources whendeploying and supporting complex infrastructure solutions. Ensuring the security of container systems andcontainerization in general, as well as the use of smart attacks based on artificial intelligence by malefactors,is a serious problem on the way to the safe and stable operation of container systems. This articleproposes an approach for detecting not only previously unknown individual anomalous processes, but alsoanomalous process sequences in container systems. The proposed approach and its implementation basedon the Docker platform are based on tracing system calls, constructing histograms of running processes,and using the AE-LSTM neural network. The process of constructing histograms is based on accounting ofthe number of executed system calls for each individual process. This solution provides the ability not onlyto accurately identify any process in the system, but also to effectively detect anomalous process sequenceswith a high degree of accuracy. The generated sequences are used as input data for the neural network.After completing the training process, the neural network acquires the ability to detect anomalous sequencesby comparing a given threshold of reconstruction error with the actual error level of the inputdata vector. When the neural network encounters a new input data vector, it calculates the reconstructionerror level - the difference between the expected and actual value. If this error exceeds a predetermined threshold, the system signals the presence of an anomaly in the sequence. Experiments show that the proposedapproach demonstrates high accuracy in detecting anomalous processes with a low level of falsepositive detection results. Such results confirm the effectiveness of the proposed approach. Also, the computationalcosts of training the neural network model are quite low. This allows using less powerful hardwarewithout significant performance losses. Such a solution can be trained and implemented in a newinfrastructure in a fairly short time Annealing (glass) ; Materials science ; Ion ; Optoelectronics ; Nanotechnology ; Composite material ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis ; Advanced Materials Characterization Techniques
Authors: Chengrui Wan | Yousef Heider | Bernd Markert
Source: PAMM (article, 2024-10-10)
Field: Materials Science — Polymers and Plastics

Microscale modelling of non‐woven material compression Abstract This research presents a discrete numerical model designed to simulate the uniaxial compression of non‐woven materials composed of entangled, non‐cross‐linked fibre systems. These materials undergo varying compression states, particularly during processes such as needle‐punching in their production. Understanding the microstructure at different compression states is critical to accurately simulate the macroscopic behaviour of non‐woven materials in such scenarios. The implementation of our numerical model, which uses force‐based dynamics for discrete particles, incorporates recovery forces originating from fibre elasticity and repulsive forces at fibre contacts. The frictional behaviour of the fibres is also described in the numerical model. The algorithm has been optimised for improved computational efficiency when simulating large numbers of fibres. Given the initial configuration of the fibre system, the model is capable of simulating the compressed fibre system at any compression state. It also provides a load‐compression relationship, thus serving as a homogenisation method for understanding the mechanical behaviour of such microstructural materials. Microscale chemistry ; Materials science ; Compression (physics) ; Composite material ; Mathematics Polymer Foaming and Composites ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Μάρκος Πούλος | Konstantinos Papagelis | Emmenuel N. Koukaras | G. Kalosakas | Giorgia Fugallo | Konstantinos Termentzidis
Source: arXiv (Cornell University) (preprint, 2024-06-27)
Field: Materials Science — Materials Chemistry

Temperature Dependence of Phonon Energies and Lifetimes in Single- and Few-layered Graphene In this work, we have studied the phonon properties of multi-layered graphene with the use of Molecular Dynamics (MD) simulations and the k-space Autocorrelation Sequence (k-VACS) method. We calculate the phonon dispersion curves, densities of states and lifetimes $τ$ of few-layered graphene of 1-5 layers and graphite. $Γ$-point phonon energies and lifetimes are investigated for different temperatures ranging from 80 K to 1000 K. The study focuses on the impact of the interlayer interaction and temperature on the energies and lifetimes of the $Γ$-point phonons, as well as the type of interlayer potential used. For the later we used the Kolmogorov-Crespi (KC) and the Lennard-Jones (LJ) potentials. We have found that the number of layers $N$ has little effect on the intra-layer (ZO and G) mode energies and greater effect on the inter-layer (Layer Shearing and Layer Breathing) modes, while $τ$ is generally affected by $N$ for all modes, except for the Layer Shear mode. The trend of $N$ on the lifetimes was also found to independent of the type of potential used. For the Raman-active G phonon, our calculations show that the lifetime increase with $N$ and that this increase is directly connected to the strength of the interlayer coupling and how this is modelled. Graphene ; Phonon ; Condensed matter physics ; Materials science ; Physics ; Nanotechnology Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Vikas Mahor | Prachi Singh
Source: Żywność (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

ADVANCES IN DEEP LEARNING TECHNOLOGIES FOR DETECTION AND CLASSIFICATION PROCESSES IN THE FOOD INDUSTRY Background. Deep learning (DL) stands as a highly effective big data analytics method, boasting a remarkable track record across diverse domains such as image processing, voice recognition, object detection, illness diagnosis, prediction and clinical decision support systems. Its applicability extends even further into emerging areas like food science and engineering, where it has witnessed a surge in its application. Over the past decade, DL has demonstrated its utility in various facets of the food industry, including food quality assessment, the detection, differentiation, identification of diseases, phenotyping of plant stressors, monitoring and smart farming practices. The integration of DL technologies has been pivotal in revolutionizing the food business and its associated supply chains, facilitating advancements in food quality evaluation, food recognition and spectroscopic analysis. Notably, hyperspectral imaging and acoustic data have emerged as key modalities leveraged by DL techniques in these applications. Results and conclusions. This study aims to delve into the recent strides made in Deep Learning frameworks within the food industry, exploring their diverse applications and functionalities. Of particular interest is the exploration of DL's role in food sensory analysis and consumer research, where it presents a promising avenue for sophisticated data mining techniques. Through a comprehensive examination of comparative performances, architectural nuances and potential future applications, this paper aims to shed light on the evolving landscape of DL in the realm of food science and engineering. Food industry ; Deep learning ; Artificial intelligence ; Data science ; Computer science ; Business ; Food science ; Biology Spectroscopy and Chemometric Analyses
Authors: Danilo Lusbin Ariza Rúa | Julio Hurtado Márquez | Humberto Marbello-Peña | Edisson Chavarro‐Mesa | Liliana Carranza-López
Source:  (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

2D-QSAR Study of Thiazole derivatives as 5-Lipoxygenase inhibitors Quantitative structure–activity relationship ; Thiazole ; Lipoxygenase ; Arachidonate 5-lipoxygenase ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Computer science ; Organic chemistry ; Enzyme Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Multicomponent Synthesis of Heterocycles
Authors: Rui Li | Song Wang | Chen Wang
Source: arXiv (Cornell University) (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems Preconditioning techniques are crucial for enhancing the efficiency of solving large-scale linear equation systems that arise from partial differential equation (PDE) discretization. These techniques, such as Incomplete Cholesky factorization (IC) and data-driven neural network methods, accelerate the convergence of iterative solvers like Conjugate Gradient (CG) by approximating the original matrices. This paper introduces a novel approach that integrates Graph Neural Network (GNN) with traditional IC, addressing the shortcomings of direct generation methods based on GNN and achieving significant improvements in computational efficiency and scalability. Experimental results demonstrate an average reduction in iteration counts by 24.8% compared to IC and a two-order-of-magnitude increase in training scale compared to previous methods. A three-dimensional static structural analysis utilizing finite element methods was validated on training sparse matrices of up to 5 million dimensions and inference scales of up to 10 million. Furthermore, the approach demon-strates robust generalization capabilities across scales, facilitating the effective acceleration of CG solvers for large-scale linear equations using small-scale data on modest hardware. The method's robustness and scalability make it a practical solution for computational science. Conjugate gradient method ; Conjugate ; Scale (ratio) ; Computer science ; Mathematical optimization ; Mathematics ; Algorithm ; Physics ; Mathematical analysis Thermal properties of materials ; Dielectric materials and actuators ; Ferroelectric and Piezoelectric Materials
Authors: Nicolas Sadoune | Ivan Pogorelov | Claire Edmunds | Giuliano Giudici | Giacomo Giudice | Christian D. Marciniak | Martin Ringbauer | Thomas Monz | Lode Pollet
Source: arXiv (Cornell University) (preprint, 2024-08-09)
Field: Chemistry — Spectroscopy

Learning symmetry-protected topological order from trapped-ion experiments Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation. Symmetry (geometry) ; Order (exchange) ; Ion ; Topology (electrical circuits) ; Physics ; Theoretical physics ; Mathematics ; Quantum mechanics ; Combinatorics ; Business ; Geometry Mass Spectrometry Techniques and Applications