jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dil K. Limbu | Nathan London | Md Omar Faruque | Mohammad R. Momeni
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Chemistry — Spectroscopy

h-CMD: An efficient hybrid fast centroid and quasi-centroid molecular dynamics method for the simulation of vibrational spectra Developing efficient path integral (PI) methods for atomistic simulations of vibrational spectra in heterogeneous condensed phases and interfaces has long been a challenging task. Here, we present the h-CMD method, short for hybrid centroid molecular dynamics, that combines the recently introduced fast quasi-CMD (f-QCMD) method with fast CMD (f-CMD). In this scheme, molecules that are believed to suffer more seriously from the curvature problem of CMD, e.g., water, are treated with f-QCMD, while the rest, e.g., solid surfaces, are treated with f-CMD. To test the accuracy of the newly introduced scheme, the infrared spectra of the interfacial D2O confined in the archetypal ZIF-90 framework are simulated using h-CMD compared to a variety of other PI methods, including thermostatted ring-polymer molecular dynamics (T-RPMD) and partially adiabatic CMD as well as f-CMD and experiment as reference. Comparisons are also made to classical MD, where nuclear quantum effects are neglected entirely. Our detailed comparisons at different temperatures of 250-600 K show that h-CMD produces O-D stretches that are in close agreement with the experiment, correcting the known curvature problem and red-shifting of the stretch peaks of CMD. h-CMD also corrects the known issues associated with too artificially dampened and broadened spectra of T-RPMD, which leads to missing the characteristic doublet feature of the interfacial confined water, rendering it unsuitable for these systems. The new h-CMD method broadens the applicability of f-QCMD to heterogeneous condensed phases and interfaces, where defining curvilinear coordinates for the entire system is not feasible. Centroid ; Molecular dynamics ; Spectral line ; Dynamics (music) ; Computer science ; Physics ; Statistical physics ; Computational physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Acoustics ; Quantum mechanics Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Ye Cheng
Source: Institutional Repositories DataBase (IRDB) (article, 2024-09-01)
Field: Materials Science — Polymers and Plastics

Development of Biomass-based Polyurethanes and their Application as Adhesives Adhesive ; Materials testing ; Component (thermodynamics) ; Welding Polymer composites and self-healing ; Lignin and Wood Chemistry ; Carbon dioxide utilization in catalysis
Authors: Aleksandra Deptuch | Anna Paliga | Anna Drzewicz | Marcin Piwowarczyk | Magdalena Urbańska | Ewa Juszyńska‐Gałązka
Source: arXiv (Cornell University) (preprint, 2024-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Crystallization kinetics of equimolar liquid crystalline mixture and its components The new equimolar mixture comprises liquid crystalline compounds MHPOBC and partially fluorinated 3F2HPhF6. The phase sequence of the mixture is determined by differential scanning calorimetry, polarizing optical microscopy, X-ray diffraction, and broadband dielectric spectroscopy. The enantiotropic smectic A*, C*, and CA* phases are observed for the mixture. Only partial crystallization of the mixture is observed during cooling at 2-40 K/min and the remaining smectic CA* phase undergoes vitrification. In contrast, the crystallization of the pure components is complete or almost complete for the same range of cooling rates. The kinetics of the non-isothermal and isothermal crystallization of the mixture and pure components are investigated by differential scanning calorimetry. The non-isothermal data are analyzed by the isoconversional method, while the isothermal data are analyzed using the Avrami model. Typically, the nucleation-controlled crystallization kinetics are observed. Kinetics ; Crystallization ; Chemical engineering ; Chemistry ; Materials science ; Thermodynamics ; Chromatography ; Organic chemistry ; Physics ; Engineering Liquid Crystal Research Advancements
Authors: D.O. Chervakov | Viktor Ved | V.V. Fedan | К.М. Sukhyy | О. В. Черваков
Source: Voprosy Khimii i Khimicheskoi Tekhnologii (article, 2024-12-01)
Field: Materials Science — Polymers and Plastics

The influence of solid-state polycondensation of polyethylene terephthalate on its rheological properties The influence of solid-state polycondensation conditions on the rheological properties of polyethylene terephthalate was determined. Mathematical models describing the dependence of the melt flow rate of both virgin and recycled polyethylene terephthalate on the time and temperature of the solid-state polycondensation process were developed. A correlation between the melt flow rate and the average molecular weight of polyethylene terephthalate was demonstrated. Experimental studies showed that conducting solid-state polycondensation of recycled polyethylene terephthalate at temperatures ranging from 1300C to 1600C for 3 hours produces polymeric materials with higher molecular weight and, consequently, improved performance properties. Polyethylene terephthalate ; Condensation polymer ; Rheology ; Materials science ; Solid-state ; Chemical engineering ; Polymer science ; Polymer chemistry ; Polyethylene ; Composite material ; Polymer ; Chemistry ; Physical chemistry Polymer crystallization and properties
Authors: Yuuki Kubo | Ryuhei Sato | Yuansheng Zhao | Takahiro Ishikawa | Shinji Tsuneyuki
Source: arXiv (Cornell University) (preprint, 2024-05-16)
Field: Materials Science — Materials Chemistry

Data-Assimilated Crystal Growth Simulation for Multiple Crystalline Phases To determine crystal structures from an X-ray diffraction (XRD) pattern containing multiple unknown phases, a data-assimilated crystal growth (DACG) simulation method has been developed. The XRD penalty function selectively stabilizes the structures in the experimental data, promoting their grain growth during simulated annealing. Since the XRD pattern is calculated as the Fourier transform of the pair distribution function, the DACG simulation can be performed without prior determination of the lattice parameters. We applied it to C (graphite and diamond) and SiO$_2$ (low-quartz and low-cristobalite) systems, demonstrating that the DACG simulation successfully reproduced multiple crystal structures. Crystal growth ; Crystal (programming language) ; Materials science ; Crystallography ; Computer science ; Chemistry ; Programming language Crystallization and Solubility Studies
Authors: Hai Zeng | Shuo Zou | Zhou Wang | Ziyu Li | Kangjian Luo | Yongkang Luo
Source: arXiv (Cornell University) (preprint, 2024-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic properties and enhanced magnetocaloric effect in EuAl$_3$Si single crystals This study presents systematic investigations into the growth and physical properties of EuAl$_3$Si single crystals, encompassing magnetic, transport, and thermodynamic analyses. EuAl$_3$Si undergoes a ferromagnetic transition at $T_C$ = 15 K. A significant reversible magnetocaloric effect was observed around $T_C$. Strikingly, with a small change of magnetic field 2 T, the maximum values of magnetic entropy change (13.4 J/kg K), refrigerant capacity (166 J/kg) and adiabatic temperature change (7.2 K) are found. These parameters respectively are 60%, 148% and 64% larger than those of the parent compound EuAl$_4$, and suggest EuAl$_3$Si as an excellent candidate for magnetic refrigeration applications near the temperature of liquid hydrogen. The possible mechanism for this enhancement is also discussed. Magnetic refrigeration ; Single crystal ; Materials science ; Crystallography ; Chemistry ; Physics ; Magnetization ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Magnesium Oxide Properties and Applications ; Thermal Expansion and Ionic Conductivity
Authors: Jingjing Bi | Minhao Chen | Pengpeng Nie | Yuanfang Liu | Jun Liu | Yuguo Du
Source: Molecules (article, 2024-11-21)
Field: Chemistry — Organic Chemistry

Stereoselective Total Synthesis of Natural Decanolides Bellidisin C and Pinolidoxin A divergent total synthesis of bioactive, naturally occurring decanolides, pinolidoxin and bellidisin C, was accomplished by taking advantage of chiral templates <i>L</i>-ribose and <i>L</i>-malic acid. In particular, bellidisin C, which is the first total synthesis so far, was achieved through a cascade reaction of reductive elimination and nucleophilic addition in a one-pot process and a sodium-alkoxide-promoted intramolecular lactonization as the key steps. Total synthesis ; Stereoselectivity ; Natural (archaeology) ; Chemistry ; Stereochemistry ; Organic chemistry ; Biology ; Catalysis Synthetic Organic Chemistry Methods ; Marine Sponges and Natural Products ; Microbial Natural Products and Biosynthesis
Authors: Miguel Spínola | Sonia Saxena | Prateek Gupta | Brandon Runnels | Dennis M. Kochmann
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Materials Science — Materials Chemistry

Finite-temperature grain boundary properties from quasistatic atomistics Quasistatic process ; Grain boundary ; Materials science ; Boundary (topology) ; Mathematics ; Mathematical analysis ; Composite material ; Thermodynamics ; Physics ; Microstructure Microstructure and mechanical properties ; Metallurgy and Material Forming ; Advanced materials and composites
Authors: Carlo Lucibello | Aurora Rossi
Source: arXiv (Cornell University) (preprint, 2024-12-09)
Field: Materials Science — Materials Chemistry

GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia GraphNeuralNetworks.jl is an open-source framework for deep learning on graphs, written in the Julia programming language. It supports multiple GPU backends, generic sparse or dense graph representations, and offers convenient interfaces for manipulating standard, heterogeneous, and temporal graphs with attributes at the node, edge, and graph levels. The framework allows users to define custom graph convolutional layers using gather/scatter message-passing primitives or optimized fused operations. It also includes several popular layers, enabling efficient experimentation with complex deep architectures. The package is available on GitHub: \url{https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}. Computer science ; Graph ; Deep learning ; Enhanced Data Rates for GSM Evolution ; Theoretical computer science ; Dense graph ; Node (physics) ; Artificial intelligence ; Line graph ; Pathwidth Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Functional Brain Connectivity Studies
Authors: Zhijie Yuan | Zijun Shen | Md All Amin Newton | Binjie Xin | Jing Zhang
Source: Research Square (preprint, 2024-12-23)
Field: Materials Science — Polymers and Plastics

Objective and High-Precision Fabric Wrinkle Assessment using 3D Point Cloud Data and Deep Learning Techniques Point cloud ; Computer science ; Wrinkle ; Benchmark (surveying) ; Artificial intelligence ; Deep learning ; Repeatability ; Code (set theory) ; Machine learning ; Mathematics Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Computer Graphics and Visualization Techniques
Authors: Saïd Rahmani | Roger de Souza Lima | Eric Serris | Ana Cameirão | Johan Debayle
Source: Journal of Electronic Imaging (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Monitoring of industrial crystallization processes through image sequence segmentation and characterization To enhance control and monitoring of industrial crystallization processes, we propose an innovative nondestructive imaging method utilizing in situ 2D vision sensors. This approach enables the acquisition of 2D videos depicting crystal aggregates throughout the batch crystallization process. Our approach is built upon experimental observations, specifically regarding the process dynamics and sensor fouling. It involves dynamic segmentation of observed aggregates, from which quantitative analyses are derived. Notably, our method allows for tracking the evolution of the particle size distribution of crystal aggregates over time and the determination of the growth kinetics of crystals that agglomerate at the sensor air gap. This enables the detection of key stages in the crystallization process and the geometric characterization of crystal aggregate production. Image segmentation ; Image processing ; Computer vision ; Sequence (biology) ; Computer science ; Artificial intelligence ; Segmentation ; Crystallization ; Pattern recognition (psychology) ; Image (mathematics) ; Engineering ; Chemical engineering Crystallization and Solubility Studies
Authors: Samih Haj Ibrahim | Tomasz Wejrzanowski | Christian W. Karl | Espen Sagvolden | Jakub Karwaszewski | Monika Pilz | Bartłomiej Przybyszewski | Rafał Kozera
Source: Materials (article, 2024-12-17)
Field: Materials Science — Surfaces, Coatings and Films

Novel Numerical Method for Studying Water Freezing on Surfaces Texturized by Laser Within this study, a methodology for the numerical simulation of droplet freezing, including a micrometer texturized pattern, was developed. The finite volume method was then applied to simulate the behavior of water droplets. The procedure was divided into two processes: stabilization and freezing. In the stabilization step, the droplet was dropped onto the material surface and took an equilibrium shape. In the second step, additional energy equation and temperature boundary conditions were applied to perform freezing simulation. Based on the laser-texturized samples of polyurethane-coated metal substrates studied with freezing delay experiments, numerical models were generated, and droplet freezing simulations were performed. Three cases were studied-non-texturized and texturized with respectively linear and triangular patterns. The obtained simulation results of freezing time were compared with experimental measurements to evaluate the proposed methodology. The study revealed that despite the inability to predict accurate freezing delay time, the proposed methodology can be used to compare the freezing delay capabilities for different texturized patterns. Additionally, the proposed model renders it possible to analyze additional aspects of wetting and freezing of the droplet on rough surfaces, which may be helpful in understanding these processes. Materials science ; Mechanics ; Wetting ; Thermodynamics ; Composite material ; Physics Surface Modification and Superhydrophobicity ; Icing and De-icing Technologies ; Fluid Dynamics and Heat Transfer
Authors: Karan Shah | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2024-07-12)
Field: Materials Science — Surfaces, Coatings and Films

Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, we present a novel approach to accelerate real time TDDFT based electron dynamics simulations using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters. Propagator ; Dynamics (music) ; Electron ; Statistical physics ; Physics ; Computer science ; Nuclear physics ; Quantum mechanics ; Acoustics Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Surface and Thin Film Phenomena
Authors: Byron Ganazhapa | Javier Pereiro García | Xabier Quintana Arregui | Morten Andreas Geday | Gonzalo Guadaño | Manuel Caño‐García
Source: Research Square (preprint, 2024-04-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generation of Arbitrarily Patterned Polarizers Using 2-Photon Polymerization Polarizer ; Photoresist ; Dichroic glass ; Materials science ; Optics ; Optoelectronics ; Nanotechnology ; Physics ; Layer (electronics) Liquid Crystal Research Advancements ; Nonlinear Optical Materials Studies ; Photochromic and Fluorescence Chemistry
Authors: Jin Kousaka | Atsuko H. Iwane | Yuichi Togashi
Source: arXiv (Cornell University) (preprint, 2024-05-10)
Field: Materials Science — Surfaces, Coatings and Films

Automated Cell Structure Extraction for 3D Electron Microscopy by Deep Learning Modeling the 3D structures of cells and tissues is crucial in biology. Sequential cross-sectional images from electron microscopy provide high-resolution intracellular structure information. The segmentation of complex cell structures remains a laborious manual task for experts, demanding time and effort. This bottleneck in analyzing biological images requires efficient and automated solutions. In this study, the deep learning-based automated segmentation of biological images was explored to enable accurate reconstruction of the 3D structures of cells and organelles. An analysis system for the cell images of Cyanidioschyzon merolae, a primitive unicellular red algae, was constructed. This system utilizes sequential cross-sectional images captured by a focused ion beam scanning electron microscope (FIB-SEM). A U-Net was adopted and training was performed to identify and segment cell organelles from single-cell images. In addition, the segment anything model (SAM) and 3D watershed algorithm were employed to extract individual 3D images of each cell from large-scale microscope images containing numerous cells. Finally, the trained U-Net was applied to segment each structure within these 3D images. Through this procedure, the creation of 3D cell models could be fully automated. The adoption of other deep learning techniques and combinations of image processing methods will also be explored to enhance the segmentation accuracy further. Electron microscope ; Extraction (chemistry) ; Microscopy ; Artificial intelligence ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Optics ; Chromatography Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Jacob J. Wardzala | Daniel S. King | Laura Gagliardi
Source: ChemRxiv (preprint, 2024-12-25)
Field: Chemistry — Organic Chemistry

Automated Multireference Vertical Excitations for Transition Metal Compounds Excited states of transition metal complexes are generally strongly correlated due to the near-degeneracy of the metal d orbitals. Consequently, electronic structure calculations of such species often necessitate multireference approaches. However, widespread use of multireference methods is hindered due to the active space selection problem, which has historically required system-specific chemical knowledge and a trial-and-error approach. Here, we address this issue with an automated method combining the approximate pair coefficient (APC) scheme for estimating orbital entropies with the discrete variational selection (DVS) approach for evaluating active space quality. We apply DVS-APC to the calculation of 67 vertical excitations in transition metal diatomics as well as to two larger complexes. We show DVS-APC generated active spaces yield NEVPT2 mean absolute errors of 0.18 eV, in line with previous accuracies obtained for organic systems, but larger than errors achieved with hand-selected active spaces (0.14 eV). If instead of using DVS we identify the best results from our trial wave functions, we find improved performance (mean absolute error of 0.1 eV) over the manually selected results. We highlight this deviation between DVS and hand selected active spaces as a possible measure of bias introduced when hand selecting active spaces. However, we find that multiconfiguration pair-density functional theory (MC-PDFT) using the tPBE and tPBE0 functionals is roughly 0.15 eV less accurate than NEVPT2 across this class of diatomic systems, potentially accounting for the decreased performance of DVS-APC, which uses MC-PDFT energies to select between active spaces. We also showcase an ability to ``down-sample'' the DVS-APC wave functions using natural orbital occupancies to achieve smaller minimal active spaces which retain the accuracy of the larger starting active spaces. Finally, DVS-APC and tPBE0 are proven to be effective when applied to modeling excited states in two larger transition metal complexes, suggesting that the transition metal diatomics may be a particular outstanding challenge for DVS-APC and MC-PDFT approaches. Diatomic molecule ; Complete active space ; Excited state ; Atomic physics ; Chemistry ; Degeneracy (biology) ; Physics ; Density functional theory ; Computational chemistry ; Quantum mechanics ; Molecule ; Basis set ; Bioinformatics Free Radicals and Antioxidants ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Source: Communications Chemistry (article, 2024-09-19)
Field: Chemistry — Physical and Theoretical Chemistry

Women in chemistry: Q&amp;A with Professor Shixuan Du Shixuan Du is a Professor at the Institute of Physics, Chinese Academy of Sciences, in China. Shixuan’s research focuses on the interface properties and assembly mechanism of molecules on substrates, and the design of novel low-dimensional materials by using first-principle computational methods based on density functional theory as the main research tools. Chemistry History and advancements in chemistry
Authors: Debora Monego | Matthias Brosz | Johanna Buck | Vsevolod Viliuga | Jaewoon Jung | Torsten Stuehn | Matthias Schmies | Yuji Sugita | Frauke Gräter
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-12)
Field: Materials Science — Biomaterials

ColBuilder: Flexible structure generation of crosslinked collagen fibrils Abstract Collagen fibrils are fundamental building blocks of connective tissues, yet generating accurate molecular models of their structure remains challenging due to their hierarchical organization and complex crosslinking patterns. ColBuilder has been developed to automate the generation of atomistic models of crosslinked collagen fibrils and facilitate the setup of molecular simulations. The tool integrates homology modeling, higher-order structure generation and optimization to build complete fibril structures with precise control over sequence composition, crosslinking patterns, and dimensions. Users can explore different collagen sequences, manipulate crosslink chemistry through mixed ratios and densities, and generate fibrils of varying diameter and length. All-atom molecular dynamics simulations of 335 nm-long fibrils validate the generated structures, showing excellent agreement with experimental measurements of D-band periodicity and force-extension behavior. ColBuilder is available both as an open-source command-line application and through a web interface at colbuilder.mpip-mainz.mpg.de. Fibril ; Collagen fibril ; Molecular dynamics ; Materials science ; Homology modeling ; Computer science ; Biological system ; Nanotechnology ; Biophysics ; Chemistry ; Computational chemistry ; Biology Collagen: Extraction and Characterization ; Bone health and osteoporosis research ; Protein Structure and Dynamics