jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Victor Quétu | Liao, Zhu | Nour Hezbri | Pizzati, Fabio | Enzo Tartaglione
Source: arXiv (Cornell University) (preprint, 2024-06-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

LaCoOT: Layer Collapse through Optimal Transport Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their deployment on resource-constrained devices, preventing their widespread adoption. In this paper, we present an optimal transport-based method to reduce the depth of over-parametrized deep neural networks, alleviating their computational burden. More specifically, we propose a new regularization strategy based on the Max-Sliced Wasserstein distance to minimize the distance between the intermediate feature distributions in the neural network. We show that minimizing this distance enables the complete removal of intermediate layers in the network, achieving better performance/depth trade-off compared to existing techniques. We assess the effectiveness of our method on traditional image classification setups and extend it to generative image models. Our code is available at https://github.com/VGCQ/LaCoOT. Layer (electronics) ; Geology ; Environmental science ; Computer science ; Materials science ; Nanotechnology Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: М. А. Борик | A.S. Chislov | A. V. Kulebyakin | Е. Е. Ломонова | Filipp Milovich | В. А. Мызина | Vladimir Pankratov | Alexandr Poselennov | P.A. Ryabochkina | N. Yu. Tabachkova | Nataliya Sidorova | Д.М. Захаров | Dmitriy Kiselev
Source: Preprints.org (preprint, 2024-07-12)
Field: Materials Science — Ceramics and Composites

Effect of Ceria doping on the Mechanical Properties and Phase Stability of Partially Samaria-Stabilized Zirconia Crystals The effect of ceria doping of (ZrO2)1-x(Sm2O3)x crystals on their phase composition, microhardness and fracture toughness has been studied. The (ZrO2)0.995-х(Sm2O3)х(СеО2)0.005 crystals (where x = 0.032, 0.037 and 0.04) have been grown using directional melt crystallization in cold skull. The phase composition and structure of the crystals have been studied using X-ray diffraction, TEM and Raman spectroscopy. The mechanical properties such as microhardness and fracture toughness have been studied using Vickers indentation. It has been shown that the (ZrO2)0.995-х(Sm2O3)х(СеО2)0.005 solid solution crystals contain both Се4+ and Се3+ ions. Phase analysis data suggest that СеО2 doping increases the tetragonality degree of the transformable t phase and reduces the tetragonality degree of the non-transformable t' phase as compared to the (ZrO2)1-x(Sm2O3)x crystals. As a result, the t&amp;rarr;m phase transition triggered by the indentation-induced stress in the СеО2 doped crystals is more intense and covers greater regions. СеО2 doping of the solid solutions increases the fracture toughness of all the crystals studied whereas the microhardness of the crystals changes but slightly. СеО2 doping of the (ZrO2)1-х(Sm2O3)х solid solutions in the experimental concentration range does not improve the high-temperature phase stability of the crystals and does not prevent high-temperature degradation of their fracture toughness. Materials science ; Fracture toughness ; Indentation hardness ; Doping ; Raman spectroscopy ; Indentation ; Phase (matter) ; Crystallization ; Cubic zirconia ; Composite material ; Solid solution ; Crystallography ; Chemical engineering ; Microstructure ; Metallurgy ; Ceramic ; Chemistry ; Optics Advanced ceramic materials synthesis ; Nuclear materials and radiation effects ; Advanced materials and composites
Authors: Lakshmi Vineela Nalla | Aarika Kanukolanu | Madhuri Yeduvaka | Siva Nageswara Rao Gajula
Source:  (preprint, 2024-10-09)
Field: Chemistry — Spectroscopy

Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer not-yet-known not-yet-known not-yet-known unknown Triple-negative breast cancer (TNBC) represents a particularly aggressive and complex form of breast cancer lacking targeted treatment options. Its extensive intratumoral heterogeneity drives disease progression and complicates therapeutic responses. The emergence of single-cell proteomics has revolutionized biological research, expanding beyond conventional deep sequencing and imaging techniques. Proteins, as essential cellular components, offer a more direct link to phenotype and cell behavior than other biomolecules, underscoring the importance of advancing single-cell proteomic techniques for deeper insights into fundamental biology and disease mechanisms. In recent years, combining the advancements in sample preparation techniques with the power of mass spectrometry boosted the development of single-cell proteomics research, complementing transcriptomic approaches despite inherent challenges like the inability to amplify proteins akin to transcripts. In this review, we discussed the current advancements in single-cell proteomics, covering workflow, sample preparation techniques, and recent advances in mass spectrometry-based techniques. Toward the end, we explore the challenges and future directions that the scientific community must address to advance this technology. We conclude by urging the rapid resolution of these problems, enabling single-cell proteomics to become part of a robust, high-throughput, and scalable single-cell multi-omics platform that can provide deep biological insights into the diagnosis and treatment of TNBC. Proteomics ; Computational biology ; Triple-negative breast cancer ; Systems biology ; Workflow ; Quantitative proteomics ; Computer science ; Biology ; Breast cancer ; Cancer ; Bioinformatics ; Genetics Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cancer Genomics and Diagnostics
Authors: Hastono Wijaya | Femiana Gapsari | Abdul Mudjib Sulaiman | Afifah Harmayanti | Alvadro Barasa | Janu Andrean | Sa Bashkaran Adi Warman | Willy Kriswardhana | Azimatun Naimah
Source: International Journal of Mechanical Engineering Technologies and Applications (article, 2024-06-15)
Field: Materials Science — Surfaces, Coatings and Films

SUPERHYDROPHOBIC AND ANTIBACTERIAL COATINGS ON VARIOUS COTTON FABRICS USING ZNO AND AESO Superhydrophobic coatings on cotton utilized in medical applications like hospital gowns and bed linens to offer a protective barrier against fluids and bacteria. Masks were worn with different types of materials. In this study, various cotton employed ZnO and AESO to effectively decrease the surface energy of cotton fabric via a Schiff base reaction. This chemical transformation resulted in the formation of a textured surface structure that exhibited robust adhesion qualities. The study demonstrates that the superhydrophobic coating on silk fabric increases 153. 59%. The coating on silk provides a reference for fabric types with ZnO and AESO coatings. Coating ; Materials science ; Adhesion ; SILK ; Composite material ; Nanotechnology Surface Modification and Superhydrophobicity
Authors: Flavio Dipietrangelo | Francesco Nicassio | Gennaro Scarselli
Source: Aerotecnica Missili & Spazio (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

SHM Implementation on a RPV Airplane Model Based on Machine Learning for Impact Detection Abstract In this study, an on-working structural health monitoring system for impact detection on remote piloted vehicle (RPV) airplane is proposed. The approach is based on the propagation of Lamb waves in metallic structures on which Pb[Zr x Ti 1− x ]O 3 (PZT) sensors are bonded for receiving vibrational signals due to impact events. The proposed method can be used to detect impacts in aerospace structures, i.e. skin fuselage and/or wing panels. After the detection, machine learning (ML) algorithms (polynomial regression and neural networks) are applied for processing the acquired ultrasounds waves in order to characterise the impacts, in terms of time of flight (ToF) and relative location. Several test cases are studied: the ML models are tested both without external noise (in laboratory) and introducing external RC engine vibration (on-working conditions). Furthermore, this work presents the implementation of a mini-equipment for acquisition and data processing based on Raspberry Pi. A good agreement between laboratory and in-flight results is achieved, in terms of distance between the actual and calculated impact location. Airplane ; Computer science ; Artificial intelligence ; Reliability engineering ; Machine learning ; Engineering ; Aerospace engineering High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Machine Fault Diagnosis Techniques
Authors: Gihan Ketawala | Caitlin M. Reiter | Petra Fromme | Sabine Botha
Source: Journal of Applied Crystallography (article, 2024-02-12)
Field: Materials Science — Materials Chemistry

The <i>Pixel Anomaly Detection Tool</i>: a user-friendly GUI for classifying detector frames using machine-learning approaches Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code. Detector ; Computer science ; Graphical user interface ; sort ; Modular design ; Sorting ; Software ; Artificial intelligence ; Data mining ; Information retrieval ; Algorithm ; Programming language Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Machine Learning in Materials Science
Authors: Christos Gioldasis | Απόστολος Γκάμας | Costas Vlahos
Source: Polymers (article, 2024-04-17)
Field: Materials Science — Surfaces, Coatings and Films

Impact of Copolymer Architecture on Demicellization and Cargo Release via Head-to-Tail Depolymerization of Hydrophobic Blocks or Branches Utilizing molecular dynamics simulations, we explored the demicellization and cargo release dynamics of linear and miktoarm copolymers, featuring one, two, and three hydrophobic blocks or branches, each capable of head-to-tail depolymerization. Our findings revealed that, under stoichiometric trigger molecule concentrations, miktoarms with three branches exhibited consistently faster depolymerization rates than those with two branches and linear copolymers. Conversely, at constant trigger molecule concentrations, the depolymerization rates of copolymers exhibited more complex behaviors influenced by two opposing factors: the excess of trigger molecules, which increased with a decrease in the number of hydrophobic branches or blocks, and simultaneous head-to-tail depolymerization, which intensified with an increasing number of branches. Our study elucidates the intricate interplay between copolymer architecture, trigger molecule concentrations, and depolymerization dynamics, providing valuable insights for the rational design of amphiphilic copolymers with tunable demicellization and cargo release properties. Depolymerization ; Copolymer ; Amphiphile ; Molecule ; Polymer chemistry ; Molecular dynamics ; Materials science ; Chemistry ; Polymer ; Organic chemistry ; Computational chemistry Polymer Surface Interaction Studies ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Zuxin Fu | Kuan-Rong Hao | Min Guo | Jingjing He | Xiaohong Yan | Yangbo Zhou | Lei Shen | Jiaren Yuan
Source: arXiv (Cornell University) (preprint, 2024-02-25)
Field: Materials Science — Materials Chemistry

Topological skyrmions in monolayer multiferroic MoPtGe2S6 Two-dimensional (2D) multiferroic materials with coexisting ferroelectricity and ferromagnetism have garnered substantial attention for their intriguing physical properties and diverse promising applications in spintronics. For example, multiferroic materials with electronically controlled broken central symmetry provide a versatile platform for designing and manipulating topological skyrmions and diverse spintronic applications. Here, we investigate the complex magnetic properties of room-temerature multiferroic material MoPtGe2S6 and its electrical control of topological skyrmions using first-principles calculations and atomistic micromagnetic simulations. A sizable Dzyaloshinskii-Moriya interaction (DMI) (2.1 meV) is found in the multiferroic material MoPtGe2S6 with an electrically polarized ground state. The magnetic skyrmions can be stabilized in monolayer MoPtGe2S6 under zero magnetic field, and the chirality of skyrmions can be reversed with electric field-induced flipping of electrical polarization due to the reversed chirality of the DMI. Furthermore, an external magnetic fielc can reverse the magnetization direction and topological charge of the skyrmions as well as tune the size of skyrmions. These results demonstrate that the monolayer MoPtGe2S6 can enrich the 2D skyrmion community and pave the way for electronically controlled spintronic devices. Skyrmion ; Multiferroics ; Monolayer ; Condensed matter physics ; Physics ; Materials science ; Topology (electrical circuits) ; Nanotechnology ; Quantum mechanics ; Mathematics ; Ferroelectricity ; Combinatorics 2D Materials and Applications ; Multiferroics and related materials
Authors: Haiwei Zheng | Jianbin Liu | Shinji Muraishi
Source: Materials (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Optimal Arrangements and Local Anisotropy of {100} Guinier–Preston (GP) Zones by Parametric Dislocation Dynamics (PDD) Simulations Stress-oriented precipitation and the resulting mechanical anisotropy have been widely studied over the decades. However, the local anisotropy of precipitates with specific orientations has been less thoroughly investigated. This study models the interaction between an edge dislocation source and {100} variants of Guinier-Preston (GP) zones in Al-Cu alloys using the parametric dislocation dynamics (PDD) method. Concentric geometrically necessary dislocation (GND) loops were employed to construct a line integral model for thin platelets. The simulations, conducted with our self-developed code based on Green's function method and Eshelby inclusion theory revealed distinct strengthening behavior along the strong and weak directions for 60° GP zones, demonstrating anisotropic strengthening from the perspective of elastic interactions. Furthermore, the optimal inclined arrangement of the GP zone array was determined through elastic energy calculations, and these results were corroborated by TEM observations. Anisotropy ; Dislocation ; Materials science ; Parametric statistics ; Enhanced Data Rates for GSM Evolution ; Condensed matter physics ; Geometry ; Physics ; Composite material ; Optics ; Mathematics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Parsa Farzin | Mohammad Javad Hajiahmadi | Mohammad Soleimani
Source: arXiv (Cornell University) (preprint, 2024-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-channel polarization manipulation based on graphene for encryption communication Wave-based cryptography, at the vanguard of advancing technologies in advanced information science, is essential for establishing a diverse array of secure cryptographic platforms. The realization of these platforms hinges on the intelligent application of multiplexing techniques, seamlessly combined with appropriate metasurface technology. Nevertheless, existing multi-channel encryption technologies based on metasurfaces face challenges related to information leakage during partial channel decoding processes. In this paper, we present a reprogrammable metasurface for polarization modulation. This metasurface not only allows for the arbitrary customization of linearly polarized reflected waves but also enables real-time amplitude modulation. Here, relying on polarization amplitude control, a fully secure communication protocol is developed precisely in the terahertz (THZ) spectrum to achieve real-time information encryption based on polarization modulation metasurfaces where access to information is highly restricted. The proposed metasurface employs the double random phase encryption (DRPE) algorithm for information encryption. It transmits the encrypted data through different polarization channels using two graphene nanoribbons, exclusively controlled by external biasing conditions. Various encryption scenarios have been outlined to fortify information protection against potential eavesdroppers. The simulated results show that this unique technology for hiding images by manipulating the polarization of the reflected wave provides new opportunities for various applications, including encryption, THz communications, THz secure data storage, and imaging. Encryption ; Computer science ; Multiplexing ; Polarization (electrochemistry) ; Cryptography ; Electronic engineering ; Computer network ; Telecommunications ; Engineering ; Computer security Metamaterials and Metasurfaces Applications ; Wireless Communication Security Techniques ; Antenna Design and Analysis
Authors: Evan Sheridan | Lana Mineh | Raul A. Santos | Toby S. Cubitt
Source: arXiv (Cornell University) (preprint, 2024-02-28)
Field: Materials Science — Materials Chemistry

Enhancing density functional theory using the variational quantum eigensolver Quantum computers open up new avenues for modelling the physical properties of materials and molecules. Density Functional Theory (DFT) is the gold standard classical algorithm for predicting these properties, but relies on approximations of the unknown universal functional, limiting its general applicability for many fundamental and technologically relevant systems. In this work we develop a hybrid quantum/classical algorithm called quantum enhanced DFT (QEDFT) that systematically constructs quantum approximations of the universal functional using data obtained from a quantum computer. We benchmark the QEDFT algorithm on the Fermi-Hubbard model, both numerically and on data from experiments on real quantum hardware. We find that QEDFT surpasses the quality of groundstate results obtained from Hartree-Fock DFT, as well as from direct application of conventional quantum algorithms such as VQE. Furthermore, we demonstrate that QEDFT works even when only noisy, low-depth quantum computation is available, by benchmarking the algorithm on data obtained from Google's quantum computer. We further show how QEDFT also captures quintessential properties of strongly correlated Mott physics for large Fermi-Hubbard systems using functionals generated on much smaller system sizes. Our results indicate that QEDFT can be applied to realistic materials and molecular systems, and has the potential to outperform the direct application of either DFT or VQE alone, without the requirement of large scale or fully fault-tolerant quantum computers. Density functional theory ; Quantum ; Orbital-free density functional theory ; Computer science ; Physics ; Hybrid functional ; Quantum mechanics Machine Learning in Materials Science
Authors: Ahrum Son | Woojin Kim | Jongham Park | Yongho Park | Wonseok Lee | Sangwoon Lee | Hyunsoo Kim
Source: Preprints.org (preprint, 2024-08-12)
Field: Chemistry — Spectroscopy

Mass Spectrometry in Clinical Chemistry: Advancing Biomarker Discovery and Personalized Medicine Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of MS, particularly when coupled with liquid chromatography (LC-MS), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized medicine advancement. MS-based technologies have transformed precision medicine, enabling comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing detection of low-abundance molecules and elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress towards precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies. Precision medicine ; Personalized medicine ; Biomarker discovery ; Biomolecule ; Biomarker ; Computational biology ; Data science ; Mass spectrometry ; Nanotechnology ; Computer science ; Chemistry ; Proteomics ; Bioinformatics ; Medicine ; Biology ; Chromatography ; Materials science Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Haoyang Wu | A. Mark Payne | Hao‐Wei Pang | Angiras Menon | Colin A. Grambow | Duminda S. Ranasinghe | Xiaorui Dong | Alon Grinberg Dana | William H. Green
Source: ChemRxiv (preprint, 2024-02-06)
Field: Chemistry — Organic Chemistry

Towards Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions Obtaining accurate enthalpies of formation of chemical species, Hf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections (AECs) followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive Hf corrections, and fit AECs and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies. Isodesmic reaction ; Thermochemistry ; Additive function ; Quantum ; Quantum chemical ; Quantum chemistry ; Computational chemistry ; Chemistry ; Standard enthalpy of formation ; Mathematics ; Physical chemistry ; Physics ; Supramolecular chemistry ; Quantum mechanics ; Molecule ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Various Chemistry Research Topics