jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. Ф. Галиев | M.S. Ishmukhametov | N.S. Bulankin | R.V. Sharafeev | V. R. Karimov
Source: Вестник Башкирского университета (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

POLYMER-CARBON COMPOSITE MATERIAL FOR FLEXIBLE ORGANIC ELECTRONICS - . Composite number ; Materials science ; Carbon fibers ; Composite material ; Electronics ; Polymer ; Polymer science ; Engineering ; Electrical engineering Conducting polymers and applications
Authors: Weifu Cen | Xin He
Source: Scientific Journal of Technology (article, 2024-09-20)
Field: Materials Science — General Materials Science

Kinetic Study of the Effect of Cu Content on the Structure of CuZr Alloys Copper-zirconium alloys are industrial materials with significant application value. Their mechanical and thermal stability properties are determined by their microstructure and dynamic characteristics. To gain a deeper comprehension of the characteristics of copper-zirconium alloys, they have been subjected to investigation through the utilization of molecular dynamics simulations. The simulations were conducted using the Materials Studio software, with the EAM potential function employed to describe the interatomic interactions. The microcanonical system synthesis (N, V, E) was selected as the method of system generation. The atomic structure, radial distribution function, and kinetic properties of three distinct ratios of copper-zirconium alloys, namely Cu300Zr700, Cu400Zr600, and Cu500Zr500, at varying temperatures were examined through the utilization of molecular dynamics simulations. The findings indicate that the mutual free energy of CuZr alloys rises in conjunction with elevated Cu atomic content, yet exerts minimal influence on their atomic diffusion state. An escalation in temperature results in a surge in the mutual free energy of the alloys and a pronounced impact on the extent of atom diffusion within the alloy system. The results are of significant value in optimizing the properties and material design of CuZr alloys. Materials science ; Content (measure theory) ; Kinetic energy ; Psychology ; Metallurgy ; Mathematics ; Physics ; Mathematical analysis ; Classical mechanics Material Properties and Applications ; Metallurgical and Alloy Processes ; Induction Heating and Inverter Technology
Authors: Anselm W. Hahn | J. Zsombor-Pindera | Pierre Kennepohl | Serena DeBeer
Source: ACS Omega (article, 2024-05-20)
Field: Chemistry — Analytical Chemistry

Introducing SpectraFit: An Open-Source Tool for Interactive Spectral Analysis In chemistry, analyzing spectra through peak fitting is a crucial task that helps scientists extract useful quantitative information about a sample's chemical composition or electronic structure. To make this process more efficient, we have developed a new open-source software tool called SpectraFit. This tool allows users to perform quick data fitting using expressions of distribution and linear functions through the command line interface (CLI) or Jupyter Notebook, which can run on Linux, Windows, and MacOS, as well as in a Docker container. As part of our commitment to good scientific practice, we have introduced an output file-locking system to ensure the accuracy and consistency of information. This system collects input data, results data, and the initial fitting model in a single file, promoting transparency, reproducibility, collaboration, and innovation. To demonstrate SpectraFit's user-friendly interface and the advantages of its output file-locking system, we are focusing on a series of previously published iron-sulfur dimers and their XAS spectra. We will show how to analyze the XAS spectra via CLI and in a Jupyter Notebook by simultaneously fitting multiple data sets using SpectraFit. Additionally, we will demonstrate how SpectraFit can be used as a black box and white box solution, allowing users to apply their own algorithms to engineer the data further. This publication, along with its Supporting Information and the Jupyter Notebook, serves as a tutorial to guide users through each step of the process. SpectraFit will streamline the peak fitting process and provide a convenient, standardized platform for users to share fitting models, which we hope will improve transparency and reproducibility in the field of spectroscopy. Computer science ; Software ; Process (computing) ; Consistency (knowledge bases) ; Container (type theory) ; Interface (matter) ; Data mining ; Operating system ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Zhao Xu | Haiyang Yu | Montgomery Bohde | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2024-11-06)
Field: Materials Science — Materials Chemistry

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction Recent advancements in equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. Using spherical harmonics (SH) and tensor products (TP), these equivariant networks gain enhanced physical understanding, like symmetries and many-body interactions. Beyond encoding physical insights, SH and TP are also crucial to represent equivariant polynomial functions. In this work, we analyze the equivariant polynomial functions for the equivariant architecture, and introduce a novel equivariant network, named PACE. The proposed PACE utilizes edge booster and the Atomic Cluster Expansion (ACE) technique to approximate a greater number of $SE(3) \times S_n$ equivariant polynomial functions with enhanced degrees. As experimented in commonly used benchmarks, PACE demonstrates state-of-the-art performance in predicting atomic energy and force fields, with robust generalization capability across various geometric distributions under molecular dynamics (MD) across different temperature conditions. Our code is publicly available as part of the AIRS library https://github.com/divelab/AIRS/. Equivariant map ; Degree (music) ; Graph ; Mathematics ; Computer science ; Pure mathematics ; Combinatorics ; Physics Machine Learning in Materials Science ; Systems Engineering Methodologies and Applications ; Neural Networks and Applications
Authors: Vincent Dufour‐Décieux | Philipp Rehner | Johannes Schilling | Elias Moubarak | Joachim Groß | André Bardow
Source: ChemRxiv (preprint, 2024-07-18)
Field: Chemistry — Spectroscopy

Classical Density Functional Theory as a Fast and Accurate Method for Adsorption Property Prediction of Porous Materials Physical adsorption separation is vital for many industrial processes, prompting researchers to develop new materials for energy-efficient processes. Porous adsorbent materials are of particular interest due to their diverse design possibilities and computational screening has accelerated the search for optimal materials. Classical density functional theory (cDFT) has recently been used as a faster alternative to state-of-the-art computational methods for screening of porous materials. However, extensive validation of cDFT predictions has not been performed for many materials, in a wide range of conditions, and with guest molecules exhibiting strong Coulombic interactions. In this paper, we validate the cDFT predictions by calculating the adsorption properties for more than 500 Metal-Organic Frameworks with three adsorbate molecules (CH4, N2, and CO2) and comparing them to state-of-the-art results from Grand Canonical Monte Carlo (GCMC) simulations. For CO2, we introduce the computation of Coulombic interactions between the MOF and the molecule, which are necessary to accurately describe this system. Our results demonstrate cDFT's ability to accurately replicate GCMC adsorption isotherms and enthalpies of adsorption while needing a median time of only 6 minutes per material. These features position cDFT as a serious candidate for adsorption properties estimations of porous materials for a wide range of physical adsorption-based processes. Porosity ; Property (philosophy) ; Adsorption ; Density functional theory ; Porous medium ; Materials science ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Physical chemistry ; Composite material ; Philosophy ; Epistemology Adsorption, diffusion, and thermodynamic properties of materials ; Electrostatics and Colloid Interactions
Authors: Anika Tabassum
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution. Curriculum ; Battery (electricity) ; Phase (matter) ; Computer science ; Ion ; Materials science ; Mathematics education ; Chemistry ; Psychology ; Physics ; Pedagogy ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Nicolas Hayer | Thomas Specht | Justus Arweiler | Dominik Gond | Hans Hasse | Fabian Jirasek
Source: arXiv (Cornell University) (preprint, 2024-12-06)
Field: Materials Science — Materials Chemistry

Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quantifying the pairwise similarity of components, which we achieve by comparing quantum-chemical descriptors of the components, namely $σ$-profiles. The basic idea behind the approach is that mixtures with similar pairs of components will have similar thermodynamic properties. The SBM is trained on a matrix that contains some data for a given property for different binary mixtures; the missing entries are then predicted by the SBM. As an example, we consider the prediction of isothermal activity coefficients at infinite dilution ($γ^\infty_{ij}$) and show that the SBM outperforms the well-established physical methods modified UNIFAC (Dortmund) and COSMO-SAC-dsp. In this case, the matrix is only sparsely occupied, and it is shown that the SBM works also if only a limited number of data for similar mixtures is available. The SBM idea can be transferred to any mixture property and is a powerful tool for generating essential data for many applications. Imputation (statistics) ; Similarity (geometry) ; Quantum chemical ; Mathematics ; Statistics ; Computer science ; Data mining ; Artificial intelligence ; Chemistry ; Missing data ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Tirth Raj Paneru | Poonam Tandon | Bhawani Datt Joshi
Source: Journal of Nepal Physical Society (article, 2024-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical evaluation on electronic and biological properties of quercetin by quantum chemical and molecular docking method The quantum chemical method was used to present the electronic and biological properties of quercetin by enlightening specific reactive sites on the molecule, which demonstrated intra- and intermolecular hydrogen bonding. The geometrical parameters (bond lengths and bond angles) of quercetin were determined and compared to the experimental structure of monohydrate quercetin. The strong intra-molecular hydrogen bond O3-H29… O4 was justified by the quantum theory of atoms in molecules, which was also confirmed by the RDG scatter plot and isosurface. With the global minimum potential at O4 and maximum potential at H31, two groups, OH and C=O, have been investigated as the location for intermolecular hydrogen bonding for solid-state conformation and biological activity. The HOMO–LUMO gap in gaseous and solvent ethanol was measured to be 3.777 eV and 3.932 eV, respectively, confirming that quercetin is more kinetically stable in gaseous medium than solvent ethanol. Global reactivity descriptors provide additional information about the chemical behavior of quercetin in the gaseous and solvent phases. Toxicity measurement predicts that the title molecule will exhibit a third class of toxicity. Molecular docking with the protein aldose reductase reveals that quercetin has the highest binding affinity and the lowest inhibition constant, with the target protein codes 2BGS and 2VDS confirming that it may inhibit aldose reductase. Quantum chemical ; Docking (animal) ; Quercetin ; Computational chemistry ; Chemistry ; Biochemical engineering ; Nanotechnology ; Biological system ; Computational biology ; Materials science ; Molecule ; Biology ; Biochemistry ; Engineering ; Organic chemistry ; Medicine Nonlinear Optical Materials Research ; Metal complexes synthesis and properties ; Inorganic and Organometallic Chemistry
Authors: T. E. King | Franklin E. Caputo | A. N. HUGHES | Julian R. Barnes
Source: Journal of Surfactants and Detergents (article, 2024-04-01)
Field: Chemistry — Organic Chemistry

Rheological‐based digital approach for gel curve analysis of alcohol ethoxylates Abstract Many surfactants, such as higher mole alcohol ethoxylates like C12‐15–7EO and C9‐11–8EO, when diluted in water, will form a gel at specific concentrations and temperatures. Gels can be highly viscous and semi‐solid and should be avoided since they take time and energy to disperse once formed. Historically, the creation of gel diagrams or maps for our technical product brochures primarily has depended on visual observation, leading to variable interpretations and inconsistent results over time. Also, completing a gel map for one surfactant grade requires a minimum of one day, due to testing many samples across various concentrations and temperatures. To improve objectivity, consistency, and speed in gel mapping, oscillatory rheology was utilized to identify gels using viscoelastic properties by testing samples prepared at various concentrations. The digitization of the gel mapping technique provides two significant benefits. It offers a rheological‐based approach giving a non‐subjective, digital gel map and it is faster than our visual‐based method. Furthermore, this digital method is consistent with our visual‐based method giving good discrimination between surfactant grades and reproducibility within batches of the same grade. This work also demonstrates the promising potential of utilizing machine learning algorithms to model the rheological behavior of gel maps effectively. R and Python, programming languages widely used for data analysis, graphing, and machine learning, were employed. Overall, the new digital approach presented yields several benefits for surfactant gel behavior study, including a reduction in subjectivity, faster data generation, and increased efficiency in the gel map analysis process. Chemistry ; Alcohol ; Rheology ; Chromatography ; Organic chemistry ; Thermodynamics Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications
Authors: S. J. Magorrian | Anas Siddiqui | Nicholas D. M. Hine
Source: arXiv (Cornell University) (preprint, 2024-06-27)
Field: Materials Science — Materials Chemistry

Strong atomic reconstruction in twisted bilayers of highly flexible InSe: Machine-Learned Interatomic Potential and continuum model approaches The relaxation of atomic positions to their optimal structural arrangement is crucial for understanding the emergence of new physical behavior in long scale superstructures in twisted bilayers of two-dimensional materials. The amount of deviation from a rigid moiré structure will depend on the elastic properties of the constituent monolayers which for the twisted bilayer - the more flexible the monolayers are, the lower the energy required to deform the layers to maximize the areas with an energetically optimal interlayer arrangement of atoms. We investigate this atomic reconstruction for twisted bilayers of highly flexible InSe. Results using two methods are demonstrated - first we train a machine-learned interatomic potential (MLIP) to enable fully atomistic relaxations of small-twist-angle large-length-scale moiré supercells while retaining density functional theory (DFT) level accuracy. We find substantial out-of-plane corrugation and in-plane domain formation for a wide range of twist angles and moiré length scales. We then adapt an existing continuum approach and show that it can reproduce some, but not all, features of the fully atomistic calculations. Interatomic potential ; Materials science ; Nanotechnology ; Physics ; Condensed matter physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Adam Olejniczak | Yury P. Rakovich | Victor Krivenkov
Source: arXiv (Cornell University) (preprint, 2024-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements and challenges in plasmon-exciton quantum emitters based on colloidal quantum dots The Nobel Prizes in Physics (2022) and Chemistry (2023) heralded the recognition of quantum information science and the synthesis of quantum dots, respectively. This acknowledgment has propelled colloidal quantum dots and perovskite nanocrystals to the forefront of quantum technologies. Their distinct emission properties, facilitating the efficient generation of both single photons and photon pairs, render them particularly captivating. Moreover, their adaptability to diverse structures, ranging from traditional electronics to nanopatterned frameworks, underscores their pivotal role in shaping quantum technologies. Despite notable strides in synthesis, certain properties require refinement for enhanced applicability in quantum information, encompassing emission brightness, stability, single photon indistinguishability, and entanglement fidelity of photon pairs. Here we offer an overview of recent achievements in plasmon-exciton quantum emitters based on luminescent semiconductor nanocrystals. Emphasizing the utilization of the light-matter coupling phenomenon, we explore how this interaction enables the manipulation of quantum properties without altering the chemical structure of the emitters. This approach addresses critical aspects for quantum information applications, offering precise control over emission rate, intensity, and energy. The development of these hybrid systems represents a significant stride forward, demonstrating their potential to overcome existing challenges and advance the integration of quantum emitters into cutting-edge quantum technology applications. Quantum dot ; Plasmon ; Exciton ; Nanotechnology ; Quantum ; Optoelectronics ; Materials science ; Colloid ; Physics ; Chemistry ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanowire Synthesis and Applications
Authors: Университет Альнур | Абдулкадер Фарис Абдулкадер | Лэйт Бакир Салман | Университетский колледж Аль-Мансура | Юсиф Фуад Наср | Университетский колледж Аль-Хикма | Наталья Боднар | Университетский колледж Аль-Рафидаин | Рана Хикмат | Университет Аль-Турат
Source: Radioelectronics Nanosystems Information Technologies (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Graphene Based Composites Material Characteristics and Industrial Uses Приведены оценки и анализ механических характеристик композитов на основе графена, а также изучено их практическое применение в различных отраслях. Проведена оценка литературы, в которой особое внимание уделялось достижениям в производстве композитов на основе графена, характеристике и применении. Собраны экспериментальные данные нескольких исследований для оценки улучшения механических характеристик, таких как прочность на разрыв, эластичность и ударопрочность, благодаря включению графена. Показано, что композиты на основе графена имеют гораздо лучшие механические характеристики, чем композиты без графена. Степень увеличения варьируется в зависимости от материала матрицы, содержания графена и используемого производственного процесса. Graphene ; Materials science ; Composite material ; Nanotechnology Graphene research and applications ; Fiber-reinforced polymer composites ; Material Selection and Properties
Authors: Joyce Custer | Matias Kalaswad | R. Kothari | Paul G. Kotula | Timothy Ruggles | Alejandro Hinojos | Rémi Dingreville | Amelia Henriksen | David P. Adams
Source: Integrating materials and manufacturing innovation (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Sputter-Deposited Mo Thin Films: Characterization of Grain Structure and Monte Carlo Simulations of Sputtered Atom Energies and Incidence Angles Sputtering ; Materials science ; Monte Carlo method ; Sputter deposition ; Thin film ; Nanotechnology Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Joseph P. Heindel | R. Allen LaCour | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2024-05-03)
Field: Chemistry — Electrochemistry

The Role of Charge in Microdroplet Redox Chemistry In charged water microdroplets, which occur in nature or in the lab upon ultrasonication or in electrospray processes, the thermodynamics for reactive chemistry can be dramatically altered relative to the bulk phase. Here, we provide a theoretical basis for the observation of accelerated chemistry by simulating water droplets of increasing charge imbalance to create redox agents such as hydroxyl and hydrogen radicals and solvated electrons. We compute the hydration enthalpy of OH^- and H^+ that controls the electron transfer process, and the corresponding changes in vertical ionization energy and vertical electron affinity of the ions, to create OH* and H* reactive species. We find that at ~20-50% of the Rayleigh limit of droplet charge the hydration enthalpy of both OH^- and H^+ have decreased by &gt;50 kcal/mol such that electron transfer becomes thermodynamically favorable, in correspondence with the more favorable vertical electron affinity of H^+ and the lowered vertical ionization energy of OH^-. We provide scaling arguments that show that the nanoscale calculations and conclusions extend to the experimental microdroplet length scale. The relevance of the droplet charge for chemical reactivity is illustrated for the formation of H2O2, and has clear implications for other redox reactions observed to occur with enhanced rates in microdroplets. Redox ; Chemistry ; Charge (physics) ; Nanotechnology ; Chemical physics ; Physics ; Inorganic chemistry ; Materials science ; Quantum mechanics Electrochemical Analysis and Applications ; Free Radicals and Antioxidants ; Electrochemical sensors and biosensors