jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junho Kim | Yeachan Kim | Jun-Hyung Park | Yerim Oh | Suho Kim | SangKeun Lee
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science We introduce a novel continued pre-training method, MELT (Materials-aware continued pretraining), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science.Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the training strategy, given that materials science corpus has distinct characteristics from other domains.To this end, we first construct a comprehensive materials knowledge base from the scientific corpus by building semantic graphs.Leveraging this extracted knowledge, we integrate a curriculum into the adaptation process that begins with familiar and generalized concepts and progressively moves toward more specialized terms.We conduct extensive experiments across diverse benchmarks to verify the effectiveness and generality of MELT.A comprehensive evaluation convincingly supports the strength of MELT, demonstrating superior performance compared to existing continued pretraining methods.In-depth analysis of MELT also shows that MELT enables PLMs to effectively represent materials entities compared to the existing adaptation methods, thereby highlighting its broad applicability across a wide spectrum of materials science 1 . Adaptation (eye) ; Computer science ; Training (meteorology) ; Language model ; Natural language processing ; Psychology ; Meteorology Machine Learning in Materials Science
Authors: Kumpei Shiraishi | Ludovic Berthier
Source: The Journal of Physical Chemistry B (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Characterizing the Slow Dynamics of the Swap Monte Carlo Algorithm The swap Monte Carlo algorithm introduces nonphysical dynamic rules to accelerate the exploration of the configuration space of supercooled liquids. Its success raises deep questions regarding the nature and physical origin of the slow dynamics of dense liquids and how it is affected by swap moves. We provide a detailed analysis of the slow dynamics generated by the swap Monte Carlo algorithm at very low temperatures in two glass-forming models. We find that the slowing down of the swap dynamics is qualitatively distinct from its local Monte Carlo counterpart, with considerably suppressed dynamic heterogeneity at both single-particle and collective levels. Our results suggest that local kinetic constraints are drastically reduced by swap moves, leading to nearly Gaussian and diffusive dynamics and weakly growing dynamic correlation length scales. The comparison between static and dynamic fluctuations shows that swap Monte Carlo is a nearly optimal local equilibrium algorithm, suggesting that further progress should necessarily involve collective or driven algorithms. Swap (finance) ; Monte Carlo method ; Algorithm ; Computer science ; Statistical physics ; Monte Carlo algorithm ; Mathematics ; Physics ; Statistics ; Economics Material Dynamics and Properties ; Theoretical and Computational Physics ; Advanced Thermodynamics and Statistical Mechanics
Authors: Christian Gerloff | Meryem A. Yücel | Lena Mehlem | Kerstin Konrad | Vanessa Reindl
Source: Neurophotonics (article, 2024-11-04)
Field: Chemistry — Electrochemistry

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection. Computer science ; Artificial intelligence ; Machine learning ; Thresholding ; Unsupervised learning ; Detector ; Pattern recognition (psychology) ; Functional near-infrared spectroscopy ; Channel (broadcasting) ; Detection theory ; Supervised learning ; Relevance (law) ; Artificial neural network ; Telecommunications Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Denny Indrajaya | Hanna Arini Parhusip | Suryasatriya Trihandaru | Djoko Hartanto
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2024-03-23)
Field: Materials Science — Polymers and Plastics

MobileNetV2-D and multiple cameras for swiftlet nest classification based on feather intensity MobileNetV2-D is a modified version of MobileNetV2, which is the novelty of this article. The algorithm is used to classify swiftlet nests into seven classes. In 2023, PT Waleta Asia Jaya is required to achieve a 7-fold increase in the export quota of swiftlet nests. To meet the quota, the company made a machine that can recognize swiftlet nest objects, which are classified into seven classes based on feather intensity, namely BRS, BR, BST, BS, BBT, BB, and BB2 for the light feathers to the heavy feathers, respectively. The input image is a combination of four images from four cameras with different positions, which adds to the novelty of MobileNetV2-D for the particular problem here. From the evaluation that has been carried out, the accuracy value of the MobileNetV2-D model was better than the MobileNetV2 model, i.e., the accuracy value of the MobileNetV2-D model was 99.9928% for the training dataset and 94.0723% for the testing dataset. Moreover, the speed of MobileNetV2-D is better than MobileNetV2- architecture. Feather ; Nest (protein structural motif) ; Computer vision ; Intensity (physics) ; Artificial intelligence ; Geography ; Computer science ; Zoology ; Cartography ; Biology ; Optics ; Physics Textile materials and evaluations
Authors: Roland Léonel Nkepsu Mbitou | Alain Dequidt | Florent Goujon | Benoit Latour | Julien Devémy | Nicolas Martzel | Patrice Hauret | Patrice Malfreyt
Source: The Journal of Chemical Physics (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Development of anisotropic force fields for homopolymer melts at the mesoscale With the aim of producing realistic coarse-grained models of homopolymers, we introduce a tabulated backbone-oriented anisotropic potential. The parameters of the model are optimized using statistical trajectory matching. The impact of grain anisotropy is evaluated at different coarse-graining levels using cis-polybutadiene as a test case. We show that, at the same time, tuning the aspect ratio of the grains can lead to a better density and structure and may reduce the unphysical bond crossings by up to 90%, without increasing the computation time too much and thereby jeopardizing the main advantage of coarse-grained models. Anisotropy ; Granularity ; Computation ; Statistical physics ; Mesoscale meteorology ; Trajectory ; Matching (statistics) ; Polybutadiene ; Materials science ; Algorithm ; Computer science ; Physics ; Polymer ; Mathematics ; Composite material ; Statistics ; Optics Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; Rheology and Fluid Dynamics Studies
Authors: Jincheng Qing | Shicai Wang | Shuyi Gu | Lin Lin | Qinpei Xie | Daming Li | Wen Huang | Junxiong Guo
Source: Sensors (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Graphene–PbS Quantum Dot Heterostructure for Broadband Photodetector with Enhanced Sensitivity Photodetectors converting light into electrical signals are crucial in various applications. The pursuit of high-performance photodetectors with high sensitivity and broad spectral range simultaneously has always been challenging in conventional semiconductor materials. Graphene, with its zero bandgap and high electron mobility, is an attractive candidate, but its low light absorption coefficient restricts its practical application in light detection. Integrating graphene with light-absorbing materials like PbS quantum dots (QDs) can potentially enhance its photodetection capabilities. Here, this work presents a broadband photodetector with enhanced sensitivity based on a graphene-PbS QD heterostructure. The device leverages the high carrier mobility of graphene and the strong light absorption of PbS QDs, achieving a wide detection range from ultraviolet to near-infrared. Employing a simple spinning method, the heterostructure demonstrates ultrahigh responsivity up to the order of 10<sup>7</sup> A/W and a specific detectivity on the order of 10<sup>13</sup> Jones, showcasing significant potential for photoelectric applications. Photodetector ; Graphene ; Optoelectronics ; Photodetection ; Materials science ; Responsivity ; Heterojunction ; Quantum dot ; Photoelectric effect ; Semiconductor ; Nanotechnology Graphene research and applications ; Quantum Dots Synthesis And Properties ; Graphene and Nanomaterials Applications
Authors: Uwe Monkowius | Margit Kriechbaum | Günther J. Redhammer | Raphael J. F. Berger
Source: Zeitschrift für Naturforschung B (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

The hare and the hedgehog – Similar thermal expansion of argento- and aurophilic contacts for different reasons Abstract This study investigates the thermal expansion properties of argentophilic and aurophilic contacts in coordination complexes of the form [( i Pr NHC) 2 Ag]ClO 4 and [( i Pr NHC) 2 Au]ClO 4 . Using X-ray crystallography distinct structural changes and phase transitions at varying temperatures are revealed. Both complexes exhibit a similar strong temperature dependence of the metallophilic distances. Analysis shows that the shallower, more anharmonic aurophilic interaction is counterbalanced by the smaller vibrational amplitude of the heavier gold atoms, resulting in comparable thermal elongation despite differing atomic dynamics. Anharmonicity ; Crystallography ; Thermal ; Thermal expansion ; Chemistry ; Amplitude ; Phase (matter) ; Chemical physics ; Materials science ; Condensed matter physics ; Thermodynamics ; Physics ; Optics ; Organic chemistry ; Metallurgy Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Alfred Yan | Muhammed Nur Talha Kilic | Ankit Agrawal | Roberto dos Reis | Vinayak P. Dravid
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Neural Network Models Towards Space Group Determination Using Dynamically Simulated EBSD and TKD Patterns Electron backscatter diffraction ; Group (periodic table) ; Artificial neural network ; Space (punctuation) ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Diffraction ; Physics ; Optics ; Biology Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Manilo Monaco | Angela Sileo | Diana Orlandi | Maria Libera Battagliere | Laura Candela | Mario G. C. A. Cimino | Gaetano Alessandro Vivaldi | Vincenzo Giannico
Source:  (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images Data from hyperspectral remote sensing are promising to extract and classify crop characteristics, because it provides accurate and continuous spectral signatures of crops. This paper focuses on data acquired by PRISMA, a high-resolution hyperspectral imaging satellite. Due to this large data availability, huge training datasets can be built to feed modern deep learning algorithms. This paper shows a spectral-temporal data processing based on random forest to perform feature selection, and on two-dimensional convolutional neural network to carry out classification of crops, exploiting variations in respective phenological phases during the annual life cycle. The proposed solution is described via a pilot case study, involving a field farmed with olive groves and vineyards in Apulia, Italy. Moreover, one-dimensional convolutional neural networks are used to compare classification accuracies. Early results are promising with respect to the literature. Hyperspectral imaging ; Computer science ; Segmentation ; Satellite ; Artificial intelligence ; Image segmentation ; Remote sensing ; Computer vision ; Environmental science ; Geology ; Engineering Spectroscopy and Chemometric Analyses
Authors: Seiichirou Oshima | Ken‐ichiro Suehara | Fumiya Fushimi | Hisahide Nakamura | Atsushi Hashimoto
Source: Electronics and Communications in Japan (article, 2024-05-25)
Field: Chemistry — Analytical Chemistry

Development of portable deterioration diagnosis device for oil filled transformers based on near‐infrared spectroscopic information Abstract A transformer plays a key role in electric power distribution system; therefore, when the transformer fails, several electrical components may get affected. As a result, diagnosing the deterioration of transformers is important for the management of electric power distribution system. The furfural content in the insulating oil of a transformer is widely used as an indicator of deterioration because furfural is generated by the decomposition of insulating paper. However, analyzing the furfural content is time consuming because of the requirement of a professional and the complexity of the diagnosis process. In this study, we propose a novel method for analyzing the amount of furfural in the insulating oil based on near‐infrared spectroscopic data obtained using the developed portable spectrometer, which can be used for the on‐site analysis at transformer installation sites. We selected wavenumbers with high correlation coefficients between the furfural content of oil and the spectroscopic data and estimated the furfural content by partial least squares regression. The results suggest that the furfural content can be analyzed in a few minutes using a small amount of oil through a portable diagnostic device. Furfural ; Transformer oil ; Transformer ; Distribution transformer ; Partial least squares regression ; Electric power ; Process engineering ; Materials science ; Computer science ; Electrical engineering ; Analytical Chemistry (journal) ; Engineering ; Chemistry ; Power (physics) ; Voltage ; Chromatography ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Power Transformer Diagnostics and Insulation
Authors: Tatevik Chalyan | Mehdi Feizpour | Koen Vanmol | Núria Solerdelcoll Vila | Gen Takebe | Hugo Thienpont | Heidi Ottevaere | Qing Liu
Source:  (preprint, 2024-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of two-photon polymerized periodic nanostructures for label-free SERS biosensing Surface-enhanced Raman spectroscopy (SERS) has shown its ability to characterize a biological substance down to a single-molecule level without a specific biorecognition mechanism. Various nanofabrication technologies enable SERS substrate prototyping and mass manufacturing. In this study, SERS substrate simulation, fabrication, and prototyping based on two-photon polymerization (2PP) are discussed. The developed substrates showed up to 10^6 Raman signal enhancements, comparable to those of commercial substrates. Moreover, the rapid prototyping of SERS substrates based on 2PP takes less than a minute to 2 hours, depending on the fabrication approach and aspect ratio requirements. The process is well-controlled and reproducible for achieving a uniform distribution of nanostructure arrays. The developed SERS substrates can be used for a broad range of applications and the characterization of different molecules. Biosensor ; Nanostructure ; Two-photon excitation microscopy ; Nanotechnology ; Polymerization ; Materials science ; Photon ; Physics ; Optics ; Polymer ; Fluorescence Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Photochromic and Fluorescence Chemistry
Authors: Aljaž Litrop | Jernej Klemenc | Marko Nagode | Domen Šeruga
Source: Materials (article, 2024-09-23)
Field: Materials Science — Biomaterials

Enhanced Cyclically Stable Plasticity Model for Multiaxial Behaviour of Magnesium Alloy AZ31 under Low-Cycle Fatigue Conditions Magnesium alloys, particularly AZ31, are promising materials for the modern automotive industry, offering significant weight savings and environmental benefits. This research focuses on the challenges associated with accurate modelling of multiaxial cyclic plasticity at small strains of AZ31 under low-cycle fatigue conditions. Current modelling approaches, including crystal plasticity and phenomenological plasticity, have been extensively explored. However, the existing models reach their limits when it comes to capturing the complexity of cyclic plasticity in magnesium alloys, especially under multiaxial loading conditions. To address this gap, a cyclically stable elastoplastic model is proposed that integrates elements from existing models with an enhanced algorithm for updating stresses and hardening parameters, using the hyperbolic tangent function to describe hardening and ensure a stabilised response with closed hysteresis loops for both uniaxial and multiaxial loading. The model is based on a von Mises yield surface and includes a kinematic hardening rule that promises a stable simulation of the response of AZ31 sheets under cyclic loading. Using experimental data from previous studies on AZ31 sheets, the proposed model is optimised and validated. The model shows promising capabilities in simulating the response of AZ31 sheet metal under different loading conditions. It has significant potential to improve the accuracy of fatigue simulations, especially in the context of automotive applications. Materials science ; Plasticity ; Magnesium alloy ; von Mises yield criterion ; Bauschinger effect ; Hardening (computing) ; Magnesium ; Structural engineering ; Hysteresis ; Metallurgy ; Composite material ; Finite element method ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal Forming Simulation Techniques
Authors: Purim Jarujamrus | Sanoe Chairam | Saksri Supasorn
Source: Chemistry Teacher International (article, 2024-12-29)
Field: Chemistry — Physical and Theoretical Chemistry

Portable syringe kit demonstration of gas generating reactions for upper secondary school chemistry Abstract The main purpose of this study is to develop a portable syringe experiment kit for easy demonstration of the chemical kinetics of H 2 , C 2 H 2, and CO 2 gas-generating reactions for upper secondary school chemistry classrooms. The main apparatus comprises two large (A and C) and one small (B) Luer-lock-tip syringes connected with a 3-way stopcock. Ignition is applied to test H 2 and C 2 H 2 gases. In contrast, the turbidity of lime water is used to test CO 2 gas. The effects of reactant species and concentrations on the reaction rates were demonstrated. The syringe kit was implemented through the 5E inquiry learning process for a group of 33 grade 11 students, leading to an improvement in their conceptual test scores on chemical reaction rates from 33.94 % to 78.03 %, with a normalized gain in the medium range (&lt; g = 0.67&gt;). This suggests that using the syringe kit within the 5E inquiry learning framework effectively supported students in developing a more accurate conceptual understanding of reaction rates. Chemistry ; Syringe ; Nanotechnology ; Engineering ; Materials science ; Mechanical engineering Various Chemistry Research Topics ; Science Education and Pedagogy ; Chemical Safety and Risk Management
Authors: Ethan Kane Waters | Carla Chia-ming Chen | Mostafa Rahimi Azghadi
Source: arXiv (Cornell University) (review, 2024-02-13)
Field: Chemistry — Analytical Chemistry

Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. We address these gaps in this review. We discuss that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology. Satellite ; Spectroscopy ; Computer science ; Remote sensing ; Environmental science ; Geography ; Engineering ; Aerospace engineering ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing
Authors: Nurchaya Simanjuntak | Raymond Erz Saragih | Yonky Pernando
Source: Journal of Computer System and Informatics (JoSYC) (article, 2024-05-31)
Field: Chemistry — Analytical Chemistry

Utilizing Lightweight YOLOv8 Models for Accurate Determination of Ambarella Fruit Maturity Levels In the agricultural sector, accurately determining fruit ripeness remains a crucial yet challenging task. Among intriguing Indonesian fruits, the Ambarella presents a particular difficulty. In Ambarella fruit, the peel changes from green to golden yellow as it ripens, serving as a visual indicator for optimal harvest time, thus determining the maturity is crucial for harvesting the Ambarella fruit. Traditionally, ripeness assessment relies on manual methods, which suffer from drawbacks like high labor costs, significant time investment, and inconsistency in results. This work explores the potential of employing YOLOv8, a cutting-edge deep learning model, to automate Ambarella fruit ripeness classification. This work focuses on the YOLOv8n, YOLOv8s, and YOLOv8m, lightweight models within the YOLOv8 family. Our results are promising: all three models achieved 100% accuracy on the training set, with YOLOv8s demonstrating the lowest loss at 0.00286. The web application was utilised to deploy the trained models, allowing users to upload images of Ambarella fruit and run the model for inference. Maturity (psychological) ; Horticulture ; Mathematics ; Environmental science ; Statistics ; Computer science ; Biology ; Psychology Spectroscopy and Chemometric Analyses