jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthias Neumann | Sven E. Wetterauer | Markus Osenberg | André Hilger | Phillip Gräfensteiner | Amalia Wagner | Nicole Bohn | Joachim R. Binder | Ingo Manke | Thomas Carraro | Volker Schmidt
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A Data-Driven Modeling Approach to Quantify Morphology Effects on Transport Properties in Nanostructured Nmc Particles Morphology (biology) ; Materials science ; Nanotechnology ; Geology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Christopher Daggett | Kaijie Yang | Chao‐Xing Liu | Lukas Muechler
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Chemistry — Physical and Theoretical Chemistry

Towards a Topological Classification of Nonadiabaticity in Chemical Reactions The application of topology, a branch of mathematics, to the study of electronic states in crystalline materials has had a revolutionary impact on the field of condensed matter physics. For example, the development of topological band theory has delivered new approaches and tools to characterize the electronic structure of materials, resulting in the discovery of new phases of matter with exotic properties. In the framework of topological band theory, the crossings between energy levels of electrons are characterized by topological invariants, which predict the presence of topological boundary states. Given the frequency of energy level crossings on the potential energy surface in molecules, the applicability of these concepts to molecular systems could be of great interest for our understanding of reaction dynamics. However, challenges arise due to differing quantum mechanical descriptions of solids and molecules. Out work aims to bridge the gap between topological band theory and molecular chemistry. We propose that the Euler Class, a topological invariant, can be used to categorize and analyse the distribution of nonadiabatic couplings on the potential energy surface. To exemplify this connection, we introduce a model system with two distinct regimes that are characterized by different values of the Euler Class, yet identical potential energy surfaces. Contrary to expectations set by the Born-Oppenheimer approximation, we propose that these two regimes don't exhibit identical dynamics, due to a qualitatively distinct distribution of nonadiabatic couplings. Topology (electrical circuits) ; Invariant (physics) ; Topological dynamics ; Physics ; Surface (topology) ; Euler characteristic ; Theoretical physics ; Quantum mechanics ; Chemistry ; Mathematics ; Geometry ; Pure mathematics History and advancements in chemistry ; Surface Chemistry and Catalysis
Authors: Xudong Wang | Wen Zhou | Hangming Zhang | Shehzad Ahmed | Tiankuo Huang | Riccardo Mazzarello | E. Ma | Wei Zhang
Source: arXiv (Cornell University) (preprint, 2023-01-09)
Field: Materials Science — Materials Chemistry

Multiscale simulations of growth-dominated Sb$_2$Te phase-change material for non-volatile photonic applications Chalcogenide phase-change materials (PCMs) are widely applied in electronic and photonic applications, such as non-volatile memory and neuro-inspired computing. Doped Sb$_2$Te alloys are now gaining increasing attention for on-chip photonic applications, due to their growth-driven crystallization features. However, it remains unknown whether Sb$_2$Te also forms a metastable crystalline phase upon nanoseconds crystallization in devices, similar to the case of nucleation-driven Ge-Sb-Te alloys. Here, we carry out ab initio simulations to understand the changes in optical properties of amorphous Sb$_2$Te upon crystallization and post annealing. During the continuous transformation process, changes in the dielectric function are highly wavelength-dependent from the visible-light range towards the telecommunication band. Our finite-difference time-domain simulations based on the ab initio input reveal key differences in device output for color display and photonic memory applications upon tellurium ordering. Our work serves as an example of how multiscale simulations of materials can guide practical photonic phase-change applications. Photonics ; Chalcogenide ; Materials science ; Nucleation ; Crystallization ; Amorphous solid ; Dielectric ; Metastability ; Annealing (glass) ; Chalcogenide glass ; Optoelectronics ; Photonic crystal ; Tellurium ; Phase-change memory ; Nanotechnology ; Crystallography ; Chemistry ; Physics ; Thermodynamics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Huda ahmed younis | Raheem Jameel Mahesein
Source: Journal of Kufa for Chemical Sciences (article, 2023-12-10)
Field: Materials Science — Polymers and Plastics

Synthesis and characterization A derivative of levofloxacin and a study of its bacteriostatic activity This study includes two newly synthesized prodrugs of Levofloxacin derivatives mono and dipeptide H2, where was the Synthesized monoPeptide as Levofloxacin-Histadine (L-H), as well as dipeptide H6 as Synthesis of diPeptide (phenylalanine- levofloxacin (GPA-L), using a Levofloxacin substituted. Spectroscopic data were studied for two derivative compounds H2 and H6 and reactivity indices were characterized using techniques FT-IR, 1H-NMR, and 13C-NMR. All the newly produced derivative compounds H2 and H6 have FT-IR spectra that share similarities in certain fingerprint-like bands and other bands. The essential functional group vibration bands. In the DMSO-d6 solvent, the compound H2, and H6 of 1H NMR spectra were studied, Typically, the fitted intensity ratio of the observed compound 1H NMR spectra gives, and the expected signals. The DMSO-d6 solvent was employed to record the 13C NMR spectra of levofloxacin derivatives H2 and H6. portrayed the 13C NMR spectra of generated compounds H1 through H6, comprised of the 13C NMR data. where done Further confirmation of the properties of the developed molecule emerged from 13C-NMR spectra. 1&#x0D; The bioactivity was studied for two derivative compounds H2 and H6 against three gram-positive and three gram-negative Bacteria. The mono and dipeptide prodrugs that are derivatives of levofloxacin have appeared to have excellent results against these Bacteria. Antibiotic susceptibility experimental findings revealed that various bacteria had taken different approaches to the tested drugs. most bacteria isolated in gram-positive and gram-negative demonstrated severe sensitivity to the generated compounds (H2 and H6). The spectral results in the FT-IR analyses as well as the nuclear magnetic resonance studies showed the high validity of the compounds formed ( H2 and H6) interaction, as well as the biological results showed a clear positive in killing bacteria of the type of Gram-negative and Gram-Positive. Dipeptide ; Levofloxacin ; Chemistry ; Carbon-13 NMR ; Proton NMR ; NMR spectra database ; Derivative (finance) ; Stereochemistry ; Spectral line ; Antibiotics ; Amino acid ; Biochemistry Polymer Synthesis and Characterization ; DNA and Nucleic Acid Chemistry ; Cancer therapeutics and mechanisms
Authors: Aparna P. A. Subramanyam | Jan Jenke | Alvin Noe Ladines | Ralf Drautz | Thomas Hammerschmidt
Source: arXiv (Cornell University) (preprint, 2023-03-11)
Field: Materials Science — Materials Chemistry

Parameterization protocol and refinement strategies for accurate and transferable analytic bond-order potentials: Application to Re Interatomic potentials provide a means to simulate extended length and time scales that are outside the reach of ab initio calculations. The development of an interatomic potential for a particular material requires the optimization of the parameters of the functional form of the potential. We present a parameterization protocol for analytic bond-order potentials (BOP) that provide a physically transparent and computationally efficient description of the interatomic interaction. The parameterization protocol of the BOP follows the derivation of the BOP along the coarse-graining of the electronic structure from density-functional theory (DFT) to the tight-binding (TB) bond model to analytic BOPs. In particular, it starts from TB parameters that are obtained by downfolding DFT eigenstates of two-atomic molecules to an $sd$-valent minimal basis. This $sd$-valent Hamiltonian is combined with a pairwise repulsion to obtain an initial binding energy relation. The $s$ electrons are then removed from the Hamiltonian and instead represented by an isotropic embedding term. In the final step, the parameters of the remaining $d$-$d$ interaction, the pair repulsion and the embedding term are optimized simultaneously. We demonstrate that the application of this parameterization protocol leads to a basic BOP for Re with good transferability. We discuss different strategies to refine the basic BOP towards global transferability or towards local accuracy. We demonstrate that homogeneous samplings of the structural phase-space in a map of local atomic environments can be used to systematically increase the global transferability. We also demonstrate the influence of training data-weighting on local accuracy refinements with a Pareto-front analysis and suggest further requirements to select a final BOP. The local accuracy and global transferability of the final BOP is also shown and compared to DFT. Hamiltonian (control theory) ; Isotropy ; Transferability ; Embedding ; Statistical physics ; Weighting ; Pairwise comparison ; Ab initio ; Density functional theory ; Chemistry ; Computer science ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Jakub Jopek | Marek Góral | Barbara Kościelniak | Kamil Ochal | Marcin Drajewicz | Magdalena Mokrzycka | Tadeusz Kubaszek | Kamil Dychtoń | Kamil Gancarczyk | Andrzej Gradzik | P. Kwaśniewski | W. Głuchowski
Source: MANUFACTURING TECHNOLOGY (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

The Influence of Industrial-Scale Pack-Boroding Process Time on Thickness and Phase Composition of Selected Cold-Work Tool Steels Jakub Jopek, Marek GĂłral, Barbara Koscielniak, Kamil Ochal, Marcin Drajewicz, Magdalena Mokrzycka, Tadeusz Kubaszek, Kamil Dychton, Kamil Gancarczyk, Andrzej Gradzik, Pawel Kwasniewski, Wojciech Gluchowski Work (physics) ; Scale (ratio) ; Composition (language) ; Phase (matter) ; Metallurgy ; Materials science ; Process (computing) ; Process engineering ; Mechanical engineering ; Computer science ; Engineering ; Chemistry ; Geography ; Art ; Cartography Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Lakshmi Shenoy | Christopher D. Woodgate | J. B. Staunton | Albert P. Bartók | Charlotte Becquart | Christophe Domain | James R. Kermode
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy We have developed a new machine learned interatomic potential for the prototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussian approximation potential (GAP) framework. This new GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first-principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (Spin GAP), and demonstrate how this extended model successfully predicts structural distortions due to antiferromagnetic and paramagnetic spin states. We demonstrate the application of the Spin GAP model for bulk properties and vacancies and validate against DFT. These results are a step towards modelling the atomistic origins of ageing in austenitic steels with higher accuracy. Antiferromagnetism ; Austenite ; Spins ; Alloy ; Materials science ; Condensed matter physics ; Spin (aerodynamics) ; Gaussian ; Density functional theory ; Interatomic potential ; Paramagnetism ; Statistical physics ; Physics ; Molecular dynamics ; Thermodynamics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Azemi, Nur Amirah Aniesya | Mohamad Taib, Mohamad Fariz | Hussin, Nur Hafiz
Source: UiTM Institutional Repositories (Universiti Teknologi MARA) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

The effect of la concentration on the structural and electronic properties of tetragonal pzt using first principles calculation The effect of Lanthanum (La) concentration on the structural and electronic properties of tetragonal Lead Zirconate Titanate, (PZT) were investigated in this study using first-principles calculation. Since lead (Pb) is recognised as a toxic substance with serious consequences for human health and the environment, La-doped PZT was selected to be examined. Lead-free elements have been considered potential substitute materials for ferroelectric components in PZT. This study aims to investigate and understand the structural and electronic properties of pure PZT. La was chosen as a dopant to study its effect on the electronic and structural PZT properties. First-principle calculations based on density functional theory (DFT) incorporated into the CASTEP software were used to accomplish the computations. Different exchange-correlation functional approximations, such as the Local Density Approximation (LDA-CAPZ) and the Generalised Gradient Approximation (GGA-PBE and GGA-PBEsol), were used to determine the geometry optimisation of ferroelectric phase materials. The lattice parameter and structure volume values calculated by GGA-PBE for PZT were the most precise. Additionally, the outcomes of tetragonal PZT were comparable to those of experiments. Hence, GGA-PBE was selected for La-doped PZT based on comparing the tetragonal PZT's lattice parameter with experiment data and selecting GGA-PBE as the most accurate. The best composition of La was 40%, with a lower band gap of 0.641 eV. By substituting or doping in a Pb-based system, these results will help experimentalists reduce the amount of lead they use, significantly advancing the field of green technology. Tetragonal crystal system ; CASTEP ; Ferroelectricity ; Density functional theory ; Lead zirconate titanate ; Dopant ; Doping ; Lattice constant Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Multiferroics and related materials
Authors: Wenkai Zhu | Yingmei Zhu | Tong Zhou | Xianpeng Zhang | Hailong Lin | Qirui Cui | Faguang Yan | Ziao Wang | Yongcheng Deng | Hongxin Yang | Lixia Zhao | Igor Žutić | K. D. Belashchenko | Kaiyou Wang
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Large and tunable magnetoresistance in van der Waals Ferromagnet/Semiconductor junctions Magnetic tunnel junctions (MTJs) with conventional bulk ferromagnets separated by a nonmagnetic insulating layer are key building blocks in spintronics for magnetic sensors and memory. A radically different approach of using atomically-thin van der Waals (vdW) materials in MTJs is expected to boost their figure of merit, the tunneling magnetoresistance (TMR), while relaxing the lattice-matching requirements from the epitaxial growth and supporting high-quality integration of dissimilar materials with atomically-sharp interfaces. We report TMR up to 192% at 10 K in all-vdW Fe3GeTe2/GaSe/Fe3GeTe2 MTJs. Remarkably, instead of the usual insulating spacer, this large TMR is realized with a vdW semiconductor GaSe. Integration of two-dimensional ferromagnets in semiconductor-based vdW junctions offers gate-tunability, bias dependence, magnetic proximity effects, and spin-dependent optical-selection rules. We demonstrate that not just the magnitude, but also the TMR sign is tuned by the applied bias or the semiconductor thickness, enabling modulation of highly spin-polarized carriers in vdW semiconductors. Spintronics ; Condensed matter physics ; Magnetoresistance ; Ferromagnetism ; Semiconductor ; van der Waals force ; Materials science ; Magnetic semiconductor ; Figure of merit ; Quantum tunnelling ; Epitaxy ; Optoelectronics ; Nanotechnology ; Physics ; Magnetic field ; Layer (electronics) ; Quantum mechanics 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Physics of Superconductivity and Magnetism
Authors: Yevhenii Zhuravskyi | Kweeni Iduoku | Meade Erickson | Anas Karuth | Durbek Usmanov | Gerardo M. Casañola‐Martín | Maqsud Sayfiyev | Dilshod Ziyaev | Zulayho Smanova | Bakhtiyor Rasulev
Source: ChemRxiv (preprint, 2023-06-22)
Field: Materials Science — Polymers and Plastics

A Quantitative Structure - Permittivity Relationship Study of a Series of Polymers Dielectric constant is a value, that is widely applied in many scientific fields, and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with application of a genetic algorithm combined with multiple linear regression analysis (GA-MLRA), to get a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied the models were proven to have a good predictive ability and robustness. Robustness (evolution) ; Dielectric ; Permittivity ; Computer science ; Linear regression ; Polymer ; Quantitative structure–activity relationship ; Regression analysis ; Dielectric permittivity ; Polarization (electrochemistry) ; Series (stratigraphy) ; Biological system ; Algorithm ; Data mining ; Machine learning ; Materials science ; Chemistry ; Optoelectronics Polymer crystallization and properties ; Synthesis and properties of polymers ; Analytical Chemistry and Sensors
Authors: M. Vollmar | Robert A. Nicholls | S.V. Antonyuk
Source: Acta Crystallographica Section D Structural Biology (article, 2023-12-21)
Field: Chemistry — Spectroscopy

Current trends in macromolecular model refinement and validation The Guest Editors provide an introduction to the special issue of articles based on talks at the CCP4 Study Weekend 2022, which is available at https://journals.iucr.org/special_issues/2023/CCP42022/. Computer science ; Data science ; Current (fluid) ; Engineering ethics ; Management science ; Engineering Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function ; Metabolomics and Mass Spectrometry Studies
Authors: Alejandro Morales‐Bayuelo | Jesús Sánchez‐Márquez
Source: F1000Research (preprint, 2023-12-11)
Field: Chemistry — Organic Chemistry

New findings on ligand series used as SARS-CoV-2 virus inhibitors within the frameworks of molecular docking, molecular quantum similarity and chemical reactivity indices These studies allowed for the identification of the main stabilizing interactions using the crystal structure of SARS-CoV-2 RdRp. The molecular quantum similarity and chemical reactivity descriptors provide novel insights into these ligands that can be used in the design of new COVID-19 treatments. Open peer review ; Docking (animal) ; Computational biology ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Plant biology ; Similarity (geometry) ; Ligand (biochemistry) ; Series (stratigraphy) ; Molecular dynamics ; Biology ; Virology ; Chemistry ; Medicine ; Computational chemistry ; Computer science ; Biochemistry ; Artificial intelligence ; Receptor ; Pathology ; Infectious disease (medical specialty) Free Radicals and Antioxidants ; Synthesis and biological activity ; Computational Drug Discovery Methods
Authors: Luis Balderas | Miguel Lastra | José M. Benítez
Source: arXiv (Cornell University) (preprint, 2023-12-17)
Field: Materials Science — Materials Chemistry

Can persistent homology whiten Transformer-based black-box models? A case study on BERT compression Large Language Models (LLMs) like BERT have gained significant prominence due to their remarkable performance in various natural language processing tasks. However, they come with substantial computational and memory costs. Additionally, they are essentially black-box models, challenging to explain and interpret. In this article, we propose Optimus BERT Compression and Explainability (OBCE), a methodology to bring explainability to BERT models using persistent homology, aiming to measure the importance of each neuron by studying the topological characteristics of their outputs. As a result, we can compress BERT significantly by reducing the number of parameters (58.47% of the original parameters for BERT Base, 52.3% for BERT Large). We evaluated our methodology on the standard GLUE Benchmark, comparing the results with state-of-the-art techniques and achieving outstanding results. Consequently, our methodology can "whiten" BERT models by providing explainability to its neurons and reducing the model's size, making it more suitable for deployment on resource-constrained devices. Transformer ; Computer science ; Benchmark (surveying) ; Black box ; Software deployment ; Artificial intelligence ; Software engineering ; Electrical engineering ; Engineering ; Cartography ; Geography Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Radiomics and Machine Learning in Medical Imaging