jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabian Sordon | Omar El Khatib | Robert Courant | Akshay Balachandran Jeeja | Jürgen Maas | Bjöern Kiefer
Source: Smart Materials and Structures (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models Abstract This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability. Bridging (networking) ; Constitutive equation ; Thermodynamics ; Scale (ratio) ; Materials science ; Statistical physics ; Physics ; Computer science ; Finite element method Shape Memory Alloy Transformations ; Elasticity and Material Modeling ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Yan Zhang | Hualiang Pi | Jiaxuan Liu | Wangqian Miao | Ziyue Qi | Nicolas Regnault | Hongming Weng | Xi Dai | B. Andrei Bernevig | Quansheng Wu | Jiabin Yu
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

Universal Moiré-Model-Building Method without Fitting: Application to Twisted MoTe$_2$ and WSe$_2$ We develop a comprehensive method to construct analytical continuum models for moiré systems directly from first-principle calculations without any parameter fitting. The core idea of this method is to interpret the terms in the continuum model as a basis, allowing us to determine model parameters as coefficients of this basis through Gram-Schmidt orthogonalization. We apply our method to twisted MoTe$_2$ and WSe$_2$ with twist angles ranging from 2.13$^\circ$ to 3.89$^\circ$, producing continuum models that exhibit excellent agreement with both energy bands and wavefunctions obtained from first-principles calculations. We further propose a strategy to integrate out the higher-energy degrees of freedom to reduce the number of the parameters in the model without sacrificing the accuracy for low-energy bands. Our findings reveal that decreasing twist angles typically need an increasing number of harmonics in the moiré potentials to accurately replicate first-principles results. We provide parameter values for all derived continuum models, facilitating further robust many-body calculations. Our approach is general and applicable to any commensurate moiré materials accessible by first-principles calculations. Moiré pattern ; Structural engineering ; Mathematical analysis ; Mathematics ; Geometry ; Computer science ; Physics ; Algorithm ; Engineering ; Optics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Peter III J. G. Remoto | Keith C. Gordon | Sara J. Fraser‐Miller
Source: Pharmaceuticals (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

A Quantitative Chemometric Study of Pharmaceutical Tablet Formulations Using Multi-Spectroscopic Fibre Optic Probes <b>Background/Objectives:</b> Two fibre optic probes were custom designed to perform Raman and near-infrared spectroscopic measurements. Our long-term objective is to develop a non-destructive tool able to collect data in hard-to-access locations for real-time analysis or diagnostic purposes. This study evaluated the quantitative performances of Probe A and Probe B using model pharmaceutical tablets. <b>Methods:</b> Measurements were performed using pharmaceutical tablets containing hydroxyl propylcellulose, titanium dioxide (anatase), lactose monohydrate, and indomethacin (γ form). Material content and thickness of bilayer samples (samples consisting of a top layer and a bottom layer of differing materials) were also assessed using Probe A to evaluate its capabilities to collect sub-surface information. Principal component analysis and partial least squares regression models were using individual and fused data to evaluate the performances of the different probe configurations. <b>Results:</b> Hydroxymethyl cellulose (RP2=0.98, RMSEP = 2.27% <i>w</i>/<i>w</i>) and lactose monohydrate (RP2=0.97, RMSEP = 2.96% <i>w</i>/<i>w</i>) content were most effectively estimated by near-infrared spectroscopy data collected using Probe A. Titanium dioxide (RP2=0.99, RMSEP = 0.21% <i>w</i>/<i>w</i>) content was most effectively estimated using a combination of 785 nm Raman spectroscopy and near-infrared spectroscopy using Probe B. Indomethacin (RP2=0.97, RMSEP = 1.01% <i>w</i>/<i>w</i>) was best estimated using a low-level fused dataset collected using 0 mm, 2.5 mm, and 5.0 mm lateral offsets of 785 nm spatially offset Raman spectroscopy using Probe A. <b>Conclusions:</b> The different probe configurations were able to reliably collect data and demonstrated robust quantitative performances. These results highlight the advantage of using multiple techniques for analysing different structures. Raman spectroscopy ; Materials science ; Partial least squares regression ; Analytical Chemistry (journal) ; Spectroscopy ; Near-infrared spectroscopy ; Chemistry ; Chromatography ; Optics ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Protein Interaction Studies and Fluorescence Analysis
Authors: Ke Hu | Tingting Tian | Yunfeng She | Xiaoming Guo | Lixia She | Junjie Huang | Xiaomin Huo | Xiao Liu | Zhaoting Xiong | Chao Lü
Source: Materials (article, 2024-09-30)
Field: Materials Science — Biomaterials

Comparative Study on Mechanical Properties and Microstructure Evolution of Mg-3Zn-1Mn/Sn Alloy through Ca-La-Ce Addition This study systematically investigates the influence of the composite addition of Ce, La, and Ca elements on the microstructure evolution and mechanical properties of Mg-3Zn-1Mn/Sn (wt.%) alloys. It indicates that the strength of Mg-Zn-Mn series alloys is superior to that of Mg-Zn-Sn series alloys, due to the stronger restriction of nanosized Mn particles on the recrystallization process and grain growth compared with Mg<sub>2</sub>Sn phases. The addition of the Ca-La-Ce elements significantly enhances the strength of the Mg-3Zn-1Sn alloy (YS increased by approximately 92.5%, UTS increased by approximately 29.2%, and EL decreased by nearly 52.2%), while for the Mg-3Zn-1Mn alloy, a balanced effect on both the strength and performance can be achieved. This difference mainly lies in the more pronounced refined effect on the grain size and the formation of a bimodal grain structure with strip-like un-DRXed grains and surrounding fine DRXed grains for the Mg-3Zn-1Sn alloy. In contrast, the addition of the Ca-La-Ce elements has a less obvious hindrance on the recrystallization process in the Mg-Zn-Mn series alloy, while significantly weakening the extrusion texture while refining the grains. Through in-depth characterization and experimental analysis, it is found that Sn and Ca can promote the formation of brittle and fine secondary phases. A nanoscale Sn phase (Mg<sub>2</sub>Sn phase) is more likely to accumulate at the grain boundaries, and the size of the nanoscale Ca<sub>2</sub>Mg<sub>6</sub>Zn<sub>3</sub> in Mg-Zn-Mn series alloys is finer and more dispersed than that in Mg-Zn-Sn series alloys, thus strongly hindering recrystallization and refining the recrystallized structure of the alloy. Materials science ; Alloy ; Microstructure ; Dynamic recrystallization ; Metallurgy ; Grain size ; Recrystallization (geology) ; Grain boundary ; Phase (matter) ; Extrusion ; Texture (cosmology) ; Hot working ; Chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Corrosion Behavior and Inhibition
Authors: Remco Geurts | Pavel Potocek | Noopur Jain | Ricardo Egoavil | Bert Freitag | Maurice Peemen | Yuri Rikers
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Leveraging AutoScript for Cross-Platform Deep Learning Solutions in Electron Microscopy Electron microscope ; Nanotechnology ; Deep learning ; Microscopy ; Materials science ; Computer science ; Engineering physics ; Artificial intelligence ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Ashma Ul Husna | S. S. Riaz Ahamed | M Jannat | Muhit AA | MH Islam
Source: Journal of Agriculture Food and Environment (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Characterization of Potato Leaf Disease by Digital Image Processing Technique Potato is asignificantstaple cropin Bangladesh. The productivity of potatoes decreases by factors such as disease, insect infestation, and rapidvariationsin climateconditions. The classification of potato leaf disease shows avitalrole in preventing a damageof product. To identifythe signsof disease immediatelyappearing in plant,it is essentialto use automated detection techniques. If these epidemics are identified at the initialstage and properactivityis selected, the farmerswould not suffer from significant financiallosses. In this study, the classification of diseasesof potato leaf was proposedusing a digital image processing technique. The steps followed in this technique were acquisition, pre-processingof image, segmentationof image, feature extractionfrom image, and disease classification. For image acquisition, the early blight, healthy leaf, and late blight of potatoleaf were clickedusingDSLR camera.Enhancing the contrast and removingnoise,RGB images were pre-processed. The diseased portion, normalportion, and background area wassegmented through k-means clustering. Then the diseased portion was converted into a grayscale image. Feature extractionwas done usingan algorithmknown asGray LevelCo-occurrence Matrix(GLCM). The classification of disease was done using Support Vector Machine (SVM). The proposed method achieved in identifying the early blight infected leaves, late blight infected leaves, and healthy leaves, was 95%, 76.5%, and 90%, respectively. Therefore, the proposed potato leaf disease findingby means ofimage processing may be a successful technique nowadays. Characterization (materials science) ; Image processing ; Digital image processing ; Computer vision ; Digital image ; Computer science ; Artificial intelligence ; Image (mathematics) ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Zheng Wen | Weijie Zheng | Xingyue Ma | Zhentao Pang | Yifeng Ren | Hongying Chen | Jibo Xu | Chunyan Zheng | jianyi liu | Xiaohui Liu | Yu Deng | Yuefeng Nie | Di Wu | L. Bellaïche | Yurong Yang
Source: Research Square (preprint, 2024-09-13)
Field: Materials Science — Materials Chemistry

Skyrmion nanodomains in ferroelectric-antiferroelectric solid solutions Skyrmion ; Ferroelectricity ; Antiferroelectricity ; Materials science ; Condensed matter physics ; Phase diagram ; Dipole ; Dielectric ; Crystallography ; Superlattice ; Phase (matter) ; Physics ; Optoelectronics ; Chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Daniel Vizoso | Rémi Dingreville
Source: Data in Brief (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Dataset of simulated vibrational density of states and X-ray diffraction profiles of mechanically deformed and disordered atomic structures in Gold, Iron, Magnesium, and Silicon This dataset is comprised of a library of atomistic structure files and corresponding X-ray diffraction (XRD) profiles and vibrational density of states (VDoS) profiles for bulk single crystal silicon (Si), gold (Au), magnesium (Mg), and iron (Fe) with and without disorder introduced into the atomic structure and with and without mechanical loading. Included with the atomistic structure files are descriptor files that measure the stress state, phase fractions, and dislocation content of the microstructures. All data was generated via molecular dynamics or molecular statics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. This dataset can inform the understanding of how local or global changes to a materials microstructure can alter their spectroscopic and diffraction behavior across a variety of initial structure types (cubic diamond, face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) for Si, Au, Mg, and Fe, respectively) and overlapping changes to the microstructure (i.e., both disorder insertion and mechanical loading). Cubic crystal system ; Materials science ; Microstructure ; Molecular dynamics ; Diffraction ; Silicon ; Crystallography ; Diamond ; Dislocation ; Atomic units ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Metallurgy X-ray Diffraction in Crystallography ; Ion-surface interactions and analysis ; High-pressure geophysics and materials
Authors: Siqi Chen | Pierre-Philippe Dechant | Yang‐Hui He | Elli Heyes | Edward Hirst | Dmitrii Riabchenko
Source: Advances in Applied Clifford Algebras (article, 2024-05-04)
Field: Chemistry — Spectroscopy

Machine Learning Clifford Invariants of ADE Coxeter Elements Abstract There has been recent interest in novel Clifford geometric invariants of linear transformations. This motivates the investigation of such invariants for a certain type of geometric transformation of interest in the context of root systems, reflection groups, Lie groups and Lie algebras: the Coxeter transformations. We perform exhaustive calculations of all Coxeter transformations for $$A_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>A</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math> , $$D_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>D</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math> and $$E_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>E</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math> for a choice of basis of simple roots and compute their invariants, using high-performance computing. This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning. In this paper we focus on neural network classification and principal component analysis. Since the output—the invariants—is fully determined by the choice of simple roots and the permutation order of the corresponding reflections in the Coxeter element, we expect huge degeneracy in the mapping. This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy. This paper is a pump-priming study in experimental mathematics using Clifford algebras, showing that such Clifford algebraic datasets are amenable to machine learning, and shedding light on relationships between these novel and other well-known geometric invariants and also giving rise to analytic results. Coxeter group ; Algorithm ; Artificial intelligence ; Computer science ; Machine learning ; Basis (linear algebra) ; Degeneracy (biology) ; Mathematics ; Geometry ; Combinatorics Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Algebraic and Geometric Analysis
Authors: Jiaqi He | Simon Verlinde | Tim Beliën | Ammar Alhmedi | Pieter Verboven | Bart Nicolaı̈
Source: e-Journal of Nondestructive Testing (article, 2024-01-29)
Field: Chemistry — Analytical Chemistry

Non-destructive larval infestation detection in pear fruits using deep learning and x-ray CT generated radiographs status: Published online Infestation ; PEAR ; Larva ; Radiography ; Horticulture ; Biology ; Nuclear medicine ; Medicine ; Botany ; Radiology Spectroscopy and Chemometric Analyses ; Forensic Entomology and Diptera Studies ; Advanced X-ray and CT Imaging
Authors: Juan Felipe Huan Lew-Yee | Jorge M. del Campo | Mario Piris
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

Advancing Natural Orbital Functional Calculations Through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems Natural orbital functional (NOF) theory offers a promising approach for studying strongly correlated systems at an affordable computational cost, with an accuracy comparable to highly demanding wavefunction-based methods. However, its widespread adoption in cases involving a large number of correlated electrons has been limited by the extensive iterations required for convergence. In this work, we present a disruptive approach that embeds the techniques used for optimization in deep learning within the NOF calculation, constituting a substantial advance in the scale of accessible systems. The revamped procedure is based on the adaptive momentum technique for orbital optimization, alternated with the optimization of the occupation numbers, significantly improving the computational feasibility of challenging calculations. This work represents a complete change in the size scale of the systems that can be reached using NOF theory. We demonstrate this with three examples that involve a large number of electrons: (i) the symmetric dissociation of a large hydrogen cluster, (ii) an analysis of occupancies distribution in fullerenes, and (iii) a study of the singlet-triplet energy gap in linear acenes. Notably, the hydrogen cluster calculation, featuring 1000 electrons, represents the largest NOF calculation performed to date and one of the largest strongly correlated electron calculations ever reported. This system, which serves as an ideal model for a strongly correlated Mott insulator, illustrates a metal-to-insulator transition where all electrons participate in the correlation phenomenon, offering insight in a unique challenge. We anticipate that this work will enable the practical application of NOFs to increasingly complex and intriguing systems, leveraging the method's inherent scalability and accuracy. Scale (ratio) ; Natural (archaeology) ; Electron ; Statistical physics ; Computer science ; Physics ; Geology ; Quantum mechanics ; Paleontology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Dongmei Zou
Source: Scientific and Social Research (article, 2024-06-07)
Field: Chemistry — Analytical Chemistry

Non-destructive Banana Quality Assessment and Quality and Safety Monitoring using Spectral Imaging Technology This thesis discusses a method to realize non-destructive banana quality assessment and quality and safety monitoring using spectral imaging technology. As one of the important agricultural products in China, the quality and safety of bananas have always attracted much attention. Traditional quality assessment methods often require destroying bananas, but this method uses spectral imaging technology to realize the assessment of banana quality by measuring and analyzing the spectral characteristics of bananas. At the same time, this method also utilizes spectroscopic technology to detect harmful substances in bananas to realize the safety monitoring of banana quality. The experimental results show that the method has high accuracy and reliability, and can be used as a rapid, efficient, non-destructive means of banana quality assessment and quality and safety monitoring. Quality (philosophy) ; Quality assessment ; Computer science ; Engineering ; Reliability engineering ; Evaluation methods ; Physics Spectroscopy and Chemometric Analyses
Authors: Binxuan GENG | Guowei Dai | Huan Zhang | Shengchun QI | Christine Dewi
Source: INMATEH Agricultural Engineering (article, 2024-03-31)
Field: Chemistry — Analytical Chemistry

ACCURATE NON-DESTRUCTIVE TESTING METHOD FOR POTATO SPROUTS FOCUSING ON DEFORMABLE ATTENTION Accurate potato sprout detection is the key to automatic seed potato cutting, which is important for potato quality and yield. In this paper, a lightweight DAS-YOLOv8 model is proposed for the potato sprout detection task. By embedding DAS deformable attention in the feature extraction network and the feature fusion network, the global feature context can be efficiently represented and the attention increased to the relevant pixel image region; then, the C2f_Atten module fusing Shuffle attention is designed based on the C2f module to satisfy the attention to the key feature information of the high-level abstract semantics of the feature extraction network. At the same time, the ghost convolution is introduced to improve the C2f module and convolutional module to realize the decomposition of the redundant features to extract the key features. Verified on the collected potato sprout image data set, the average accuracy of the proposed DAS-YOLOv8 model is 94.25%, and the calculation amount is only 7.66 G. Compared with the YOLOv8n model, the accuracy is 2.13% higher, and the average accuracy is 1.55% higher. In comparison to advanced state-of-the-art (SOTA) target detection algorithms, the method in this paper offers a better balance between comprehensive performance and lightweight model design. The improved and optimized DAS-YOLOv8 model can realize the effective detection of potato sprouts, meet the requirements of real-time processing, and can provide theoretical support for the non-destructive detection of sprouts in automatic seed potato cutting. Computer science ; Key (lock) ; Artificial intelligence ; Context (archaeology) ; Feature (linguistics) ; Pattern recognition (psychology) ; Feature extraction ; Embedding ; Convolution (computer science) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Julien Hurbain | Pieter Rein ten Wolde | Peter S. Swain
Source: Bioinformatics Advances (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Quantifying the nuclear localization of fluorescently tagged proteins We performed our analysis in Python; code is available at https://git.ecdf.ed.ac.uk/v1jhurba/neunet-nucloc.git. Computational biology ; Cell biology ; Biology ; Biophysics ; Chemistry Enzyme Structure and Function ; Microtubule and mitosis dynamics ; Protein Structure and Dynamics