jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lucien Laiarinandrasana | Olga Klinkova | Cristian Ovalle | Peter Cloetens | Thilo F. Morgeneyer
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2023-05-17)
Field: Materials Science — General Materials Science

Deformation and cavitation at the spherulite scale of semi-crystalline polymers Engineering stress-strain curves are generated from tensile tests on semi-crystalline thermoplastics, which may exhibit non-linearity and/or peak stress associated with striction/necking phenomenon of the specimen at the macroscopic scale.This work addresses this state of deformed specimen, on an isotactic polypropylene, where irreversible strains have led to a variable cross-sectional area along the necked region.D images in this region, obtained through Synchrotron Radiation Computed Tomography with two high resolutions are exploited.The best resolution ( pixel length = 0.05 m) allowed a better understanding of the morphology of several deformed spherulites within which polar fan arrangements are clearly detailed.Thanks to the identi cation of the boundaries of spherulite patterns, with a 0.7 m resolution, the longitudinal and transverse elongations of larger numbers of spherulites are measured.The evolution of the volumetric plastic strains due to cavitation at the spherulitic scale along the necked regions is comprehensively analysed.Volume changes at this scale are highlighted, consisting of an increase in the case of void growth followed by a decrease at large strains due to the collapse of elongated voids.The e ects of these results on the establishment of reliable constitutive model are discussed.It is found that accounting for plastic dilation is necessary for the accuracy of constitutive models. Cavitation ; Spherulite (polymer physics) ; Deformation (meteorology) ; Materials science ; Polymer ; Scale (ratio) ; Composite material ; Mechanics ; Geography ; Physics ; Cartography Material Properties and Applications
Authors: Navneet Singh | Zhen Zhang | A. K. Sood | Walter Kob | Rajesh Ganapathy
Source: arXiv (Cornell University) (preprint, 2023-02-10)
Field: Materials Science — Materials Chemistry

Intermediate-range order governs dynamics in dense colloidal liquids The conventional wisdom is that liquids are completely disordered and lack non-trivial structure beyond nearest-neighbor distances. Recent observations have upended this view and demonstrated that the microstructure in liquids is surprisingly rich and plays a critical role in numerous physical, biological, and industrial processes. However, approaches to uncover this structure are either system-specific or yield results that are not physically intuitive. Here, through single-particle resolved three-dimensional confocal microscope imaging and the use of a recently introduced four-point correlation function, we show that bidisperse colloidal liquids have a highly non-trivial structure comprising alternating layers with icosahedral and dodecahedral order, which extends well-beyond nearest-neighbor distances and grows with supercooling. By quantifying the dynamics of the system on the particle level, we establish that it is this intermediate-range order, and not the short-range order, which has a one-to-one correlation with dynamical heterogeneities, a property directly related to the relaxation dynamics of glassy liquids. Our experimental findings provide a direct and much sought-after link between the structure and dynamics of liquids and pave the way for probing the consequences of this intermediate-range order in other liquid state processes. Supercooling ; Icosahedral symmetry ; Chemical physics ; Relaxation (psychology) ; Statistical physics ; Range (aeronautics) ; Molecular dynamics ; Order (exchange) ; Materials science ; Correlation function (quantum field theory) ; Particle (ecology) ; Physics ; Chemistry ; Crystallography ; Computational chemistry ; Thermodynamics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Steffen Seitz | Christopher Lindenberg | Ronald Tetzlaff | Thomas Götz | Stephan Schlegel
Source:  (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks &lt;p&gt;Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis.&lt;/p&gt; &lt;p&gt;In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. &lt;/p&gt; &lt;p&gt;In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. &lt;/p&gt; &lt;p&gt;Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location.&lt;/p&gt; Partial discharge ; Normalization (sociology) ; Voltage ; Electrical conductor ; Insulator (electricity) ; Computer science ; Artificial neural network ; Electrical engineering ; Electronic engineering ; Physics ; Engineering ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Embedded Systems and FPGA Design
Authors: Rita Moussallem | Elissa El Rassy | Jalal Faraj | Ahmad Al Takash | Nicolas Lefèvre | Jean Luc Bailleul
Source: SPIRE - Sciences Po Institutional REpository (article, 2023-05-30)
Field: Materials Science — General Materials Science

Identifications de propriétés thermophysiques de matériaux polymères et composites en cours de transformation International audience Composite material ; Materials science Material Properties and Applications ; Materials Engineering and Processing ; Additive Manufacturing and 3D Printing Technologies
Authors: La Ode Aman | Arfan Arfan | Aiyi Asnawi
Source: International Journal of Applied Pharmaceutics (article, 2023-12-18)
Field: Chemistry — Organic Chemistry

IN SILICO STUDY OF THE SYNERGISTIC INTERACTION OF 5-FLUOROURACIL AND CURCUMIN ANALOGUES AS INHIBITORS OF B-CELL LYMPHOMA 2 PROTEIN Objective: The research objective is to explore the potential for synergistic inhibition of two curcumin analogs combined with 5-fluorouracil (5-FU) against the B-cell lymphoma 2 (BCL-2) protein. Methods: We investigated the synergistic inhibition of two curcumin analogs, namely, (1E,4E)-1,5-bis(4-hydroxyphenyl)penta-1,4-dien-3-one (AC01) and (1E,4E)-1,5-bis(3,4-dihydroxyphenyl)penta-1,4-dien-3-one (AC02), each combined with 5-FU by calculating their binding free energies and binding stability. An in silico investigation of the synergistic interaction of ligand pairs was conducted using the multiple ligand simultaneous docking (MLSD) technique with the AutoDock Vina package. The stability of interactions and binding free energies of each BCL-2 and curcumin analogs were examined by applying molecular dynamics techniques with the Gromacs package and MMPBSA method. Results: All ligand pairs had displayed strong binding affinity, as evidenced by highly negative free energy values, indicating a robust association with BCL-2. Molecular dynamics simulations were conducted over 100 ns, confirming good stability with controlled RMSD changes, suggesting that the ligand pairs had remained securely bound to the BCL-2 binding site. Additionally, RMSF analysis and energy decomposition had revealed that ligand interactions did not influence protein residue fluctuations during the simulation, and the protein-ligand complexes had maintained stability throughout the simulation. Furthermore, binding free energy calculations using the MMPBSA method had consistently shown negative values, signifying stable interactions with BCL-2 for all ligand pairs. Conclusion: In conclusion, our study revealed that AC01 and AC02, when combined with 5-FU, had the ability to intercalate into the P2 and P4 sites of BCL-2. This suggested that AC01 and AC02 held promise for further study as candidates for anticancer drugs, individually or in combination with 5-FU. In silico ; Curcumin ; Chemistry ; Lymphoma ; Pharmacology ; Fluorouracil ; Computational biology ; Cancer research ; Stereochemistry ; Biochemistry ; Biology ; Immunology ; Genetics ; Chemotherapy ; Gene Synthesis and biological activity ; Computational Drug Discovery Methods ; Glycosylation and Glycoproteins Research
Authors: Xin Li | Deng Pan | Chengyin Li | Qiang Yao | Dongxiao Zhu
Source: arXiv (Cornell University) (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Negative Flux Aggregation to Estimate Feature Attributions There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of the deep neural network architectures, explaining DNN predictions still remains as an open problem, preventing us from gaining a deeper understanding of the mechanisms. To enhance the explainability of DNNs, we estimate the input feature's attributions to the prediction task using divergence and flux. Inspired by the divergence theorem in vector analysis, we develop a novel Negative Flux Aggregation (NeFLAG) formulation and an efficient approximation algorithm to estimate attribution map. Unlike the previous techniques, ours doesn't rely on fitting a surrogate model nor need any path integration of gradients. Both qualitative and quantitative experiments demonstrate a superior performance of NeFLAG in generating more faithful attribution maps than the competing methods. Our code is available at \url{https://github.com/xinli0928/NeFLAG} Divergence (linguistics) ; Attribution ; Computer science ; Artificial neural network ; Code (set theory) ; Transparency (behavior) ; Artificial intelligence ; Feature (linguistics) ; Task (project management) ; Deep neural networks ; Machine learning ; Algorithm ; Psychology ; Computer security ; Economics ; Social psychology Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Explainable Artificial Intelligence (XAI)
Authors: Tajinder P. Singh
Source: International Journal of Advanced Chemistry Research (article, 2023-01-01)
Field: Chemistry — Spectroscopy

New techniques in liquid chromatography for biomarker discovery Liquid chromatography (LC) has evolved significantly, becoming an indispensable tool for biomarker discovery due to its high resolution, sensitivity, and versatility. This review provides an in-depth look at recent advancements in liquid chromatography techniques, focusing on their applications in biomarker discovery. We explore innovations such as ultra-high-performance liquid chromatography (UHPLC), multidimensional chromatography, and hyphenated techniques, discussing their impact on enhancing analytical performance and their contributions to identifying novel biomarkers. We also address the challenges and future directions in this rapidly evolving field. Chromatography ; Biomarker discovery ; Biomarker ; Chemistry ; Proteomics ; Biochemistry Analytical Chemistry and Chromatography
Authors: Anuradha M. Vibhakar | D. D. Khalyavin | P. Manuel | N. J. Steinke | L. Zhang | Kazunari Yamaura | Alexei А. Belik | R. D. Johnson
Source: arXiv (Cornell University) (preprint, 2023-05-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic inhomogeneities in the quadruple perovskite manganite [Y$_{2-x}$Mn$_x$]MnMnMn$_4$O$_{12}$ A combination of competing exchange interactions and substitutional disorder gives rise to magnetic inhomogeneities in the [Y$_{2-x}$Mn$_x$]MnMnMn$_4$O$_{12}$ $x = 0.23$ and $x = 0.16$ quadruple perovskite manganites. Our neutron powder scattering measurements show that both the $x = 0.23$ and $x = 0.16$ samples separate into two distinct magnetic phases; below T$_{1}$ = 120 $\pm$ 10 K the system undergoes a transition from a paramagnetic phase to a phase characterised by short range antiferromagnetic clusters contained in a paramagnetic matrix, and below T$_{2}$ $\sim$ 65 K, the system is composed of well correlated long range collinear ferrimagnetic order, punctuated by short range antiferromagnetic clusters. A sharp increase in the antiferromagnetic phase fraction is observed below $\sim$ 33 K, concomitant with a decrease in the ferrimagnetic phase fraction. Our results demonstrate that the theoretically proposed AFM phase is stabilised in the [Y$_{2-x}$Mn$_x$]MnMnMn$_4$O$_{12}$ manganites in the presence of dominant B-B exchange interactions, as predicted. Antiferromagnetism ; Manganite ; Ferrimagnetism ; Paramagnetism ; Perovskite (structure) ; Condensed matter physics ; Materials science ; Phase (matter) ; Neutron scattering ; Crystallography ; Scattering ; Ferromagnetism ; Chemistry ; Magnetization ; Physics ; Magnetic field ; Optics Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Dielectric properties of ceramics
Authors: Wei Liu | Franziska Scheibel | Nuno M. Fortunato | Imants Dirba | Tino Gottschall | Hongbin Zhang | Konstantin Skokov | Oliver Gutfleisch
Source: arXiv (Cornell University) (preprint, 2023-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The role of Debye temperature in achieving large adiabatic temperature changes at cryogenic temperatures: a case study on $Pr_2In$ The excellent magnetic entropy change ($ΔS_T$) in the temperature range of 20 $\sim$ 77 K due to the first-order phase transition makes $Pr_2In$ an intriguing candidate for magnetocaloric hydrogen liquefaction. As an equally important magnetocaloric parameter, the adiabatic temperature change ($ΔT_{ad}$) of $Pr_2In$ associated with the first-order phase transition has not yet been reported. In this work, the $ΔT_{ad}$ of $Pr_2In$ is obtained from heat capacity measurements: 2 K in fields of 2 T and 4.3 K in fields of 5 T. While demonstrating a $ΔT_{ad}$ that is not as impressive as its remarkable $ΔS_T$, $Pr_2In$ exhibits an unusual low Debye temperature ($T_D$) of around 110 K. Based on these two observations, an approach that combines the mean-field and Debye models is developed to study the correlation between $ΔT_{ad}$ and $T_D$. The role of $T_D$ in achieving large $ΔT_{ad}$ is revealed: materials with higher $T_D$ tend to exhibit larger $ΔT_{ad}$, particularly in the cryogenic temperature range. This discovery explains the absence of an outstanding $ΔT_{ad}$ in $Pr_2In$ and can serve as a tool for designing or searching materials with both a large $ΔS_T$ and a $ΔT_{ad}$. Adiabatic process ; Magnetic refrigeration ; Delta ; Condensed matter physics ; Atmospheric temperature range ; Transition temperature ; Debye model ; Thermodynamics ; Phase transition ; Materials science ; Physics ; Magnetic field ; Magnetization ; Superconductivity ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; High-pressure geophysics and materials ; Thermal Expansion and Ionic Conductivity
Authors: Andreas Haahr Larsen
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Optimal weights and priors in simultaneous fitting of multiple small-angle scattering datasets Small-angle X-ray and neutron scattering (SAXS and SANS) are powerful techniques in material science and soft matter. In this study, it was addressed how multiple SAXS or SANS datasets are best weighted when doing simultaneous fitting. Three weighting schemes were tested: (1) equal weighting of all datapoints, (2) equal weighting of each dataset through normalization with the number of datapoints, (3) weighting proportional to the information content. The weighing schemes were assessed by model refinement against synthetic data under numerous conditions. The first weighting scheme led to the most accurate parameter estimation, especially when one dataset substantially outnumbered the other(s). Furthermore, it was demonstrated that inclusion of Gaussian priors significantly improved the accuracy of the refined parameters, as compared to common practice, where each parameter is constrained uniformly within an allowed interval. Prior probability ; Weighting ; Gaussian ; Small-angle X-ray scattering ; Small-angle scattering ; Gaussian network model ; Scattering ; Computer science ; Mathematics ; Algorithm ; Artificial intelligence ; Physics ; Optics X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Junwei Li | A Qinhua | Qiusong Lan | Jintao Yang | Lijun Yun | Yuelong Xia | Chengfu yang
Source:  (preprint, 2023-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on Demand Design Method of Cross Polarization Converter Metasurface Based on Depth Generation Model As a two-dimensional electromagnetic metamaterial, the cross-polarization conversion (CPC) metasurface is thin, easy to develop, and has attracted wide attention. However, existing CPC cell surface designs still rely on inefficient full-wave numerical simulation. Although some researchers have explored deep learning CPC metasurface structure design methods, the generated metasurface patterns are of poor quality. In this paper, an on-demand design method for cross-polarization conversion metasurface based on depth-generation model is proposed. Firstly, Wasserstein generative adversarial network (WGAN) is used to reverse design CPC metasurface, and Wasserstein distance is introduced to replace JS divergence and KL divergence to optimize the target. The problem of training difficulty caused by gradient elimination of original generative adversarial network (GAN) is fundamentally solved. Secondly, in the WGAN model, U-Net architecture generator is used to generate images, which greatly improves the surface image quality of CPC. In addition, a simulator composed of convolutional neural network (CNN) is also added in this paper to carry out forward prediction of S-parameter spectrum diagram. By inputting the patterns generated by WGAN into the simulator, the corresponding S-parameter spectrum diagram is generated and compared with the real S-parameter spectrum diagram, so as to verify whether the surface patterns of generated elements meet the requirements. The depth generation model proposed in this paper organically combines the forward spectrum prediction model and the reverse CPC metasurface structure design model, so that the CPC metasurface structure satisfying the expected electromagnetic response can be designed quickly on demand. This on-demand design method is expected to promote the rapid design, fabrication and application of electromagnetic devices. Computer science ; Metamaterial ; Generative adversarial network ; Convolutional neural network ; Polarization (electrochemistry) ; Divergence (linguistics) ; Computer engineering ; Algorithm ; Deep learning ; Electronic engineering ; Topology (electrical circuits) ; Artificial intelligence ; Optics ; Engineering ; Electrical engineering ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Karin I. Öberg | Stefano Facchini | Dana E. Anderson
Source: Annual Review of Astronomy and Astrophysics (article, 2023-06-07)
Field: Chemistry — Spectroscopy

Protoplanetary Disk Chemistry Planets form in disks of gas and dust around young stars. The disk molecular reservoirs and their chemical evolution affect all aspects of planet formation, from the coagulation of dust grains into pebbles to the elemental and molecular compositions of the mature planet. Disk chemistry also enables unique probes of disk structures and dynamics, including those directly linked to ongoing planet formation. We review the protoplanetary disk chemistry of the volatile elements H, O, C, N, S, and P; the associated observational and theoretical methods; and the links between disk and planet chemical compositions. Three takeaways from this review are: ▪The disk chemical composition, including the organic reservoirs, is set by both inheritance and in situ chemistry.▪Disk gas and solid O/C/N/H elemental ratios often deviate from stellar values due to a combination of condensation of molecular carriers, chemistry, and dynamics.▪Chemical, physical, and dynamical processes in disks are closely linked, which complicates disk chemistry modeling, but these links also present an opportunity to develop chemical probes of different aspects of disk evolution and planet formation. Protoplanetary disk ; Astrobiology ; Chemistry ; Physics ; Planet ; Astrophysics Molecular Spectroscopy and Structure ; Astro and Planetary Science ; Astrophysics and Star Formation Studies
Authors: Shashank Balla | Arpit Jain
Source: Universal Research Reports (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Benchmarking NLP Pipelines for Lead Enrichment from Unstructured External Sources Enrichment of leads is the central theme of customer relationship management (CRM) and sales performance improvement by providing high-quality information to marketing and sales organizations. Despite dramatic improvements in natural language processing (NLP) application, there is vast research void for systematic benchmarking of NLP models expressly designed for lead enrichment from external, unstructured data sources such as news articles, social media tweets, and industry reports. Previous research has focused mainly on structured or semi-structured data in internal CRM databases and overlooked the complexity and inherent noise inherent in external, unstructured data environments. Such oversight limits the strength and adaptability of existing enrichment methods. This study attempts to bridge the current knowledge gap by scientifically comparing different NLP pipelines in large-scale benchmarking, determining their effectiveness, accuracy, and flexibility in handling a broad range of external textual data benchmarks. Benchmarking ; Lead (geology) ; Natural language processing ; Artificial intelligence ; Computer science ; Pipeline transport ; Engineering ; Geology ; Business Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wang Li-jun
Source: arXiv (Cornell University) (preprint, 2023-12-04)
Field: Chemistry — Spectroscopy

Sparse Full Configuration Interaction We propose an efficient deterministic method to calculate the full configuration interaction (FCI) ground state energy. This method leverages the sparseness of the Lanczos basis vectors that span the Krylov subspace associated with the Hamiltonian to rapidly calculate the lowest eigenvalue of the effective Hamiltonian formed in this basis. By iteratively performing the spanning and diagonalization steps, this method is capable of rapidly reaching chemical accuracy for a variety of strongly correlated molecules, including stretched N$_2$ and C$_2$, in merely several tens of iterations. To accelerate our algorithm, we implement parallelized sparse matrix-sparse vector multiplication, which features a novel hashing method that establishes a one-to-one correspondence between the set of full configuration Slater determinants and a set of consecutive integers. A near-linear acceleration was observed. Lanczos resampling ; Full configuration interaction ; Hamiltonian matrix ; Eigenvalues and eigenvectors ; Hamiltonian (control theory) ; Subspace topology ; Lanczos algorithm ; Algorithm ; Configuration interaction ; Basis (linear algebra) ; Computer science ; Sparse matrix ; Mathematics ; Mathematical optimization ; Symmetric matrix ; Physics ; Excited state ; Quantum mechanics ; Artificial intelligence Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies