jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilyas Khurshid | Mubashir Hassan | M. Faizan | Imran Afgan | Yacine Addad
Source: Research Square (preprint, 2024-01-30)
Field: Materials Science — Materials Chemistry

A Thermal Approach for Modelling the Concrete Ablation during Molten Corium-Concrete Interaction Abstract An accident in a nuclear power plant involving a reactor core meltdown would result in instigation of molten corium, which is a mixture of nuclear fuel, claddings and structural components. In this paper, an enthalpy porosity model is proposed to comprehensively analyze the ablation of concrete during the Molten Corium and Concrete Interaction (MCCI) process. The developed numerical model is an extension of the enthalpy-porosity model and is termed as Corium-Concrete Enthalpy Porosity Model (CCEPM). The developed CCEPM Computational Fluid Dynamic (CFD) model can predict natural convection, melting and solidification. The developed model simplifies the complex phenomena of concrete ablation and its melting by incorporating the multi-region approach. The model was implemented in OpenFOAM, by developing a new solver which couples the buoyant-driven natural convection and conjugate heat transfer solvers. The thermal modelling and heat transfer capabilities of the developed solver were verified against experimental data sets. Additionally, the effect of various boundary conditions, concrete thermal conductivity and intensity of decay heat were analyzed to study their impact on concrete ablation. We observed significant low concrete ablation, controlled temperature and velocity field, for the water-cooled boundary condition. Accordingly, the ablation of concrete decreased by 17% just by imposing the water-cooled boundary condition. Similarly, when the thermal conductivity was decreased to 0.43 and 0.13 W/m. K, the ablation of concrete reduced by 38% and 75%, respectively. Furthermore, early cooling of molten corium to decrease decay heat was found to be an effective strategy to mitigate the concrete ablation successfully by 20%. Corium ; Materials science ; Molten metal ; Thermal ; Ablation ; Nuclear engineering ; Mechanics ; Thermodynamics ; Metallurgy ; Heat transfer ; Engineering ; Physics Nuclear Materials and Properties ; Nuclear materials and radiation effects ; Advanced ceramic materials synthesis
Authors: Tomaž Tomše | Aljaž Ivekovič | Andraž Kocjan | Sašo Šturm | Kristina Žužek
Source: Advanced Engineering Materials (article, 2024-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Rapid Thermal‐Radiation‐Assisted Sintering Strategy for Nd–Fe–B‐Type Magnets The green transition has spiked demand for high‐performance sintered Nd–Fe–B permanent magnets, necessitating advanced powder consolidation technologies to enhance production efficiency. This study explores the rapid sintering methodology for an Nd–Fe–B powder using a radiation‐assisted sintering approach. The case study material is an industrially used powder, prepared through strip‐casting, hydrogen decrepitation, and jet milling, with a mean particle size of 5.5 μm. The powder is sintered to full density in a modified spark plasma sintering furnace, achieving pressureless conditions and eliminating electrical currents in the sample to preserve grain alignment and prevent decomposition of the hard‐magnetic phase. Fully dense samples are obtained with heating rates ranging from 10 to 200 °C min −1 and up to 5 min of dwell time at 1100 °C. Rapid heating results in grain size and microstructure comparable to conventionally sintered magnets prepared from the same powder, without compromising magnetic performance after postsinter annealing at 520 °C for 120 min. This sintering method contributes to a novel strategy for optimizing magnet production by utilizing efficient thermal‐radiation heat transfer. The combination of rapid heating and pressureless sintering drastically reduces heat‐up and dwell times, providing a fundamental advantage over slow conventional sintering. Materials science ; Sintering ; Magnet ; Metallurgy ; Radiation ; Thermal ; Optics ; Mechanical engineering Magnetic Properties of Alloys ; Advanced materials and composites ; Metallic Glasses and Amorphous Alloys
Authors: Takahiro Orito | Yoshihito Kuno | Ikuo Ichinose
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Chemistry — Spectroscopy

Strong and weak symmetries and their spontaneous symmetry breaking in mixed states emerging from the quantum Ising model under multiple decoherence Discovering and categorizing quantum orders in mixed many-body systems are currently one of the most important problems. Specific types of decoherence applied to typical quantum many-body states can induce a novel kind of mixed state accompanying characteristic symmetry orders, which has no counterparts in pure many-body states. We study phenomena generated by interplay between two types of decoherence applied to the one-dimensional transverse field Ising model (TFIM). We show that in the doubled Hilbert space formalism, the decoherence can be described by filtering operation applied to matrix product states (MPS) defined in the doubled Hilbert system. The filtering operation induces specific deformation of the MPS, which approximates the ground state of a certain parent Hamiltonian in the doubled Hilbert space. In the present case, such a parent Hamiltonian is the quantum Ashkin-Teller model, having a rich phase diagram with a critical lines and quantum phase transitions. By investigating the deformed MPS, we find various types of mixed states emergent from the ground states of the TFIM, and clarify phase transitions between them. In that study, strong and weak $Z_2$ symmetries play an important role, for which we introduce efficient order parameters, such as Rényi-2 correlators, entanglement entropy, etc., in the doubled Hilbert space. Quantum decoherence ; Homogeneous space ; Ising model ; Physics ; Quantum ; Symmetry (geometry) ; Quantum mechanics ; Spontaneous symmetry breaking ; Symmetry breaking ; Theoretical physics ; Quantum dissipation ; Mathematics Molecular spectroscopy and chirality ; Quantum Mechanics and Applications ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Carlos Basilio | Iván Moreno
Source:  (preprint, 2024-02-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unveiling invariant optical properties of dielectric meta-atoms The phase-shift, transmittance, and polarization properties of meta-atoms are investigated, motivated by their use as building blocks of metasurfaces used in metalenses, holograms, and beam shaping. We studied dielectric rod meta-atoms of several geometries, which included: cylinders, squares, polygons, and truncated cones. By analyzing light propagation through these meta-atoms, we show that the phase-shift introduced is independent of their cross-section shape, contrary to the expected behavior. Additionally, we show that the polarization response is independent of the shape, and that the transmittance is partially shape independent. We identify a novel dependence of phase-shift on the effective cross-section area of meta-atoms. These meta-atom transmission properties are independent of its shape if its geometry has C3 or larger rotational symmetry. This optical invariance has significant implications in the topological optimization of flat optics. Transmittance ; Dielectric ; Polarization (electrochemistry) ; Holography ; Optics ; Invariant (physics) ; Physics ; Materials science ; Phase (matter) ; Molecular physics ; Optoelectronics ; Chemistry ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Thomas G. Williams | Elizabeth J. Watkins
Source: Monthly Notices of the Royal Astronomical Society (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

<tt>McFine</tt>: <scp>python</scp>-based Monte Carlo multicomponent hyperfine structure fitting ABSTRACT Modelling complex line emission in the interstellar medium (ISM) is a degenerate high-dimensional problem. Here, we present McFine, a tool for automated multicomponent fitting of emission lines with complex hyperfine structure, in a fully automated way. We use Markov chain Monte Carlo (MCMC) to efficiently explore the complex parameter space, allowing for characterizing model denegeracies. This tool allows for both local thermodynamic equilibrium (LTE) and radiative-transfer (RT) models. McFine can fit individual spectra and data cubes, and for cubes encourage spatial coherence between neighbouring pixels. It is also built to fit the minimum number of distinct components, to avoid overfitting. We have carried out tests on synthetic spectra, where in around 90 per cent of cases it fits the correct number of components, otherwise slightly fewer components. Typically, $T_{\rm ex}$ is overestimated and $\tau$ underestimated, but accurate within the estimated uncertainties. The velocity and line widths are recovered with extremely high accuracy, however. We verify McFine by applying to a large Atacama Large Millimeter/submillimeter Array (ALMA) N$_2$H$^+$ mosaic of an high-mass star forming region, G316.75−00.00. We find a similar quality of fit to our synthetic tests, aside from in the active regions forming O-stars, where the assumptions of Gaussian line profiles or LTE may break down. To show the general applicability of this code, we fit CO(J = 2-1) observations of NGC 3627, a nearby star-forming galaxy, again obtaining excellent fit quality. McFine provides a fully automated way to analyse rich data sets from interferometric observations, is open source, and pip-installable. Physics ; Python (programming language) ; Hyperfine structure ; Monte Carlo method ; Component (thermodynamics) ; Astrophysics ; Statistical physics ; Computational physics ; Astronomy ; Statistics ; Quantum mechanics ; Operating system Machine Learning in Materials Science
Authors: Anja Schulz | Ayah G. Ahmad | Mike C Tucker
Source: arXiv (Cornell University) (preprint, 2024-09-15)
Field: Materials Science — Biomaterials

Materials Matter: Investigating Functional Advantages of Bio-Inspired Materials via Simulated Robotic Hopping In contrast with the diversity of materials found in nature, most robots are designed with some combination of aluminum, stainless steel, and 3D-printed filament. Additionally, robotic systems are typically assumed to follow basic rigid-body dynamics. However, several examples in nature illustrate how changes in physical material properties yield functional advantages. In this paper, we explore how physical materials (non-rigid bodies) affect the functional performance of a hopping robot. In doing so, we address the practical question of how to model and simulate material properties. Through these simulations we demonstrate that material gradients in the leg of a single-limb hopper provide functional advantages compared to homogeneous designs. For example, when considering incline ramp hopping, a material gradient with increasing density provides a 35% reduction in tracking error and a 23% reduction in power consumption compared to homogeneous stainless steel. By providing bio-inspiration to the rigid limbs in a robotic system, we seek to show that future fabrication of robots should look to leverage the material anisotropies of moduli and density found in nature. This would allow for reduced vibrations in the system and would provide offsets of joint torques and vibrations while protecting their structural integrity against reduced fatigue and wear. This simulation system could inspire future intelligent material gradients of custom-fabricated robotic locomotive devices. Nanotechnology ; Materials science ; Computer science Diatoms and Algae Research ; Modular Robots and Swarm Intelligence ; Electrospun Nanofibers in Biomedical Applications
Authors: Ashot Tamrazyan | Т. А. Мацеевич
Source: Buildings (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Seismic Resistance of Reinforced Concrete Building Frames Based on Interval Assessment of the Coefficient of Permissible Damage The main method for assessing the seismic resistance of buildings in the standards of most countries is the linear-spectral method. This method allows for the calculation of the spatial model of a building for seismic load in the elastic range without resorting to direct integration of the equations of motion. Nonlinear characteristics of reinforced concrete structure materials are usually considered integrally using the reduction factor. However, the values of this factor in the Russian standards are not sufficiently substantiated, as the later studies show. To determine the coefficient of permissible damage (reduction factor), six reinforced concrete frames were considered, with different parameters such as span length, number of spans, and number of floors. The design parameters of beams and columns (section sizes, reinforcement, etc.) were preliminarily selected based on the calculation using the linear-spectral method. In the second stage, numerical modeling was carried out in the OpenSEES PC to implement the pushover analysis procedure. Then, the coefficient of permissible damage was estimated by processing the capacity curves obtained on the basis of nonlinear static calculation. The value of the sought coefficients is practically not affected by the number of stores of the frame; however, with an increase in the number of spans, the coefficient K1 increases, which is explained by a decrease in the plasticity of the system. On average, for the frames under consideration, the coefficient K1 was 0.526, which is 1.5 times greater than the coefficient proposed in modern Russian standards, K1 = 0.35. The results obtained on the basis of pushover analysis are compared with the coefficients K1 determined through the values of the average degree of damage (d) of the buildings according to the modified seismic scale MMSK-86. For various types of reinforced concrete frame buildings, K1 = 0.51 was obtained. It is recommended that the coefficient K1 for reinforced concrete frame buildings should be increased to a value of at least K1 = 0.5 in the Russian standard. Structural engineering ; Reinforced concrete ; Interval (graph theory) ; Engineering ; Forensic engineering ; Geotechnical engineering ; Statistics ; Mathematics Structural mechanics and materials
Authors: Ke Huang | Xiao Li | S. Das Sarma | Fan Zhang
Source: arXiv (Cornell University) (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

Self-consistent theory for the fractional quantum anomalous Hall effect in rhombohedral pentalayer graphene The fractional quantum anomalous Hall (FQAH) effect in rhombohedral pentalayer graphene (PLG) has attracted significant attention due to its potential for observing exotic quantum states. In this work, we present a self-consistent Hartree-Fock theory for the FQAH effect in rhombohedral PLG. In particular, we focus on the convergence of the Hartree-Fock calculation with various reference fields and discuss the stability of the FQAH states in PLG. We show that the so-called charge neutrality scheme provides an unambiguous result for the Hartree-Fock calculation, as it ensures a convergence with respect to the momentum cutoff. Based on the Hartree-Fock band structure, we further carry out exact diagonalization calculations to explore the stability of the FQAH states in PLG. Our work provides an improved and unified (minimal) theoretical framework to understand the FQAH effect in rhombohedral PLG and paves the way for future experimental and theoretical studies. Trigonal crystal system ; Graphene ; Quantum Hall effect ; Fractional quantum Hall effect ; Condensed matter physics ; Physics ; Quantum ; Quantum mechanics ; Mathematical physics ; Quantum spin Hall effect ; Chemistry ; Electron ; Crystallography ; Crystal structure Graphene research and applications ; Quantum and electron transport phenomena ; Quantum Computing Algorithms and Architecture
Authors: Samuel Abreu | Xoán Mayo López | José Guilherme Milhano | Alba Soto-Ontoso
Source: arXiv (Cornell University) (preprint, 2024-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A generalized picture of colour decoherence in dense QCD media We revisit the calculation of the soft gluon emission probability off a colour-singlet $q\bar q$ system that evolves in a quark-gluon plasma. The $q\bar q$ antenna is created in the presence of a medium and then emits a soft gluon outside. The gluon emission probability is modified with respect to the vacuum baseline due to interactions with the medium during the formation of the antenna and its propagation. Previous studies disregarded the former effect and found that the medium modification to the interference pattern of the antenna was controlled by the so-called critical angle $θ_c$, that exclusively depends on medium properties. We find that accounting for medium interactions during the antenna formation enhances the total rate of emissions off the $q\bar q$ antenna. Interestingly, it also promotes the notion of a critical angle to a dynamic quantity, denoted $\tildeθ_c$, that depends on both the medium and the antenna properties and is thus different for every splitting. As a consequence, depending on the region of parameter space, colour decoherence can either be delayed or accelerated with respect to previous estimates. Quantum decoherence ; Quantum chromodynamics ; Physics ; Particle physics ; Theoretical physics ; Statistical physics ; Computer science ; Quantum mechanics ; Quantum Liquid Crystal Research Advancements ; Random lasers and scattering media ; Characterization and Applications of Magnetic Nanoparticles
Authors: В. Г. Баженов | E. Yu. Linnik | Е. В. Нагорных | D. A. Samsonova
Source: Izvestiya of Saratov University Mathematics Mechanics Informatics (article, 2024-02-20)
Field: Materials Science — General Materials Science

Numerical modeling of the processes of deformation and buckling of multilayer shells of revolution under combined quasi-static and dynamic axisymmetric loading with torsion . . . . . . . . - . 16 , . : , - . Buckling ; Rotational symmetry ; Torsion (gastropod) ; Structural engineering ; Deformation (meteorology) ; Materials science ; Mechanics ; Engineering ; Composite material ; Physics Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Structural mechanics and materials
Authors: Junjie Hu | P.J. Wu
Source: ChemRxiv (preprint, 2024-05-07)
Field: Materials Science — Materials Chemistry

Intelligent Photoresponsive Drug Delivery with CausalLanguage Models and Chemist Instruction Training As large-scale language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the design of stimulus-responsive materials used in drug delivery over the long term. To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, integrating deeper chemical insights is imperative. Our study initially contrasted the performance of Bigbrid, Gemma, GPT NeoX, etc., specifically focusing on designing photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further explored light-driven isomerisation reaction as a critical mechanism in drug delivery. Our study explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, improving their understanding of relationships involving -N=N- groups in photoisomerisa-tion transitions of light-responsive molecules. Despite progress, the limited availability of specialized domain datasets continues to be a significant challenge in maximizing the performance of large models. Chemist ; Computer science ; Training (meteorology) ; Drug ; Natural language processing ; Artificial intelligence ; Cognitive science ; Human–computer interaction ; Chemistry ; Psychology ; Pharmacology ; Medicine ; Organic chemistry Machine Learning in Materials Science
Authors: Yeonjoon Kim | Jaeyoung Cho | Hojin Jung | Lydia Meyer | Gina M. Fioroni | Christopher D. Stubbs | Keunhong Jeong | Robert L. McCormick | Peter C. St. John | Seonah Kim
Source: ChemRxiv (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Design Green Chemicals by Predicting Vaporization Properties Using Explainable Graph Attention Networks Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable. Here, we developed chemically explainable graph attention networks to predict five physical properties pertinent to performance in utilizing renewable energy: heat of vaporization (HoV), critical temperature, flash point, boiling point, and liquid heat capacity. The predictive model for HoV was trained using ~150,000 data points, considering their uncertainties and temperature dependence. Next, this model was expanded to the other properties through transfer learning to overcome the limitations due to fewer data points (700-7,500). The chemical interpretability of the model was then investigated, demonstrating that the model explains molecular structural effects on vaporization properties. Finally, the developed predictive models were applied to design chemicals that have desirable properties as efficient and green working fluids, fuels, and polymers, enabling fast and accurate screening before experiments. Vaporization ; Interpretability ; Boiling point ; Renewable energy ; Computer science ; Flash point ; Graph ; Process engineering ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Junwu Chen | Xu Huang | Hua Cheng | Yulian He | Philippe Schwaller
Source: ChemRxiv (preprint, 2024-08-06)
Field: Materials Science — Materials Chemistry

AdsMT: A multi-modal transformer for predicting global minimum adsorption energy The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the GMAE through density functional theory (DFT). Thus, we designed a novel multi-modal transformer called AdsMT to rapidly predict the GMAE based on surface graphs and adsorbate feature vectors without any site-binding information. The AdsMT model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were constructed, opening new avenues for further research on the challenging GMAE prediction task. Our AdsMT framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 eV, respectively. Beyond GMAE prediction, AdsMT's cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions. While primarily focused on heterogeneous catalyst screening, our multi-modal approach has potential applications across materials science and chemistry. Adsorption ; Modal ; Computer science ; Benchmark (surveying) ; Encoder ; Density functional theory ; Transformer ; Trustworthiness ; Materials science ; Algorithm ; Chemistry ; Computational chemistry ; Voltage ; Physics ; Physical chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion