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The equations of state (EOS) of Iridium are, for the first time, obtained by solving the high-dimension integral of partition function based on a recently developed approach of ultrahigh efficiency and precision without any artificial parameter, and the deviation of 0.25% and 1.52% from the experiments was achieved respectively for the isobaric EOS in a temperature range of 300 K-2500 K and the isothermal EOS at 300 K up to 300 GPa. Specific comparisons show that the deviation of EOS based on harmonic approximation even including anharmonic effect, manifests worse than ours by several times or even one order of magnitude, indicating that ensemble theory is the very approach to understand the thermodynamic properties of condensed matter.
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http://dx.doi.org/10.1088/1361-648X/ac93dc | DOI Listing |
Magn Reson Med
September 2025
Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain.
Purpose: (a) To design a methodology for drawing random samples of any Ensemble Average Propagator (EAP) (b) to modify the KomaMRI simulator to accommodate them as realistic spin movements to simulate diffusion MRI (dMRI) and (c) to compare these simulations with those based on the Diffusion Tensor (DT) model.
Theory And Methods: The rejection method is used for random sampling of EAPs: starting from a probability law that is easily sampled, and whose density function wraps the target EAP, samples are accepted when they lie inside the targeted region. This is used to sample the EAP as described by Mean Apparent Propagator MRI (MAP-MRI) and in Spherical Convolution (SC) based on Spherical Harmonics (SH).
J Chem Theory Comput
September 2025
Physical Biochemistry, University of Potsdam, 14476 Potsdam, Germany.
Intrinsically disordered proteins (IDPs) pose a challenge for structural characterization, as experimental methods lack the subnanometer/subnanosecond resolution to capture their dynamic conformational ensembles. Molecular dynamics (MD) simulations can, in principle, provide this information, but for the simulation of IDPs, dedicated protein and water force fields are needed, as traditional MD models for folded proteins prove inadequate for IDPs. Substantial effort was invested to develop IDP-specific force fields, but their performance in describing IDPs that undergo conformational changes─such as those induced by molecular partner binding or changes in solution environment─remains underexplored.
View Article and Find Full Text PDFChemPhotoChem
March 2025
Max Planck Research Group, Faculty of Chemistry and Pharmacy, Universidad del Atlántico, Barranquilla, 081007, Colombia.
Azocompounds are among the most important group of molecular photoswitches due to their multiple applications in various scientific areas. We studied the thermal and photochemical reactions of an azocompound with photo-induced antibiotic properties using calculations based on Kohn-Shan, Spin-Flip and time-dependent Density Functional Theory. Our primary goal is to understand the absorption spectra and isomerization pathways governing the molecule's light-controlled antibiotic activity.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
Center S3, CNR Institute of Nanoscience, via Campi 213/A, 41125 Modena, Italy.
Infrared spectroscopy is widely used to probe the structural organization of biologically relevant molecules, including peptides, proteins, and nucleic acids. The latter show significant structural diversity, and specific infrared bands provide insights into their conformational ensembles. Among DNA/RNA infrared bands, the CO stretching modes are especially useful, as they are sensitive to the distinct structural arrangements within nucleic acids.
View Article and Find Full Text PDFSci Rep
September 2025
Higher Education Department, Kabul, Afghanistan.
Machine learning plays a pivotal role in addressing real-world challenges across domains such as cybersecurity, where AI-driven methods, especially in Software-Defined Networking, enhance traffic monitoring and anomaly detection. Contemporary networks often employ models like Random Forests, Neural Networks, and Support Vector Machines to identify threats early and reinforce security. Ensemble learning further improves predictive accuracy and stability, yet many frameworks falter when confronted with noisy or contaminated data.
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