Utilizing a preliminary interatomic potential, this work represents an initial exploration into the thermomechanical behavior of NbCr solid solutions. Specifically, it examines the effect of different amounts of Cr solute, for which information in the literature is limited. The employed interatomic potential was developed according to the embedded atom model (EAM), and was trained on data derived from density functional theory calculations. While the potential demonstrated reasonable accuracy and predictive power when tested, various results highlight deficiencies and encourage further development and training. Mechanical strength, heat capacities, thermal expansion coefficients, and thermal conductivities were found to decrease with Cr content. Elastic coefficients, too, were observed to be strongly dependent on Cr composition. The Pugh embrittlement criterion was not satisfied for any of the compositions and temperatures explored. Gibbs free energy calculations performed on C14, C15, and C36 NbCr allotropes predicted the C36 structure to be the most thermodynamically favorable across all investigated temperatures and it was found that C36 becomes increasingly more stable relative to the other two phases with increased pressure. The inability of this work to accurately capture the stability of the different Laves phases is most likely due to the shortcomings in the developed potential.
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http://dx.doi.org/10.1038/s41598-024-64920-w | DOI Listing |
Molecules
December 2024
Department of Physical and Quantum Chemistry, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
We report the results of calculations of the linear polarizability and second hyperpolarizability of the H molecule in the bond dissociation process. These calculations were performed for isolated molecules, as well as molecules under spatial confinement. The spatial confinement was modeled using the external two-dimensional (cylindrical) harmonic oscillator potential.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Biochemistry and Molecular Biology Department, Dasman Diabetes Institute, Dasman 15462, Kuwait.
: The mammalian target of the rapamycin (mTOR) signaling pathway is a central regulator of cell growth, proliferation, metabolism, and survival. Dysregulation of mTOR signaling contributes to many human diseases, including cancer, diabetes, and obesity. Therefore, inhibitors against mTOR's catalytic kinase domain (KD) have been developed and have shown significant antitumor activities, making it a promising therapeutic target.
View Article and Find Full Text PDFNat Commun
January 2025
Institute for Materials Science, University of Stuttgart, D-70569, Stuttgart, Germany.
The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius diffusion behavior has remained elusive, largely due to the lack of effective computational tools. Here we propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at the density-functional-theory level.
View Article and Find Full Text PDFSci Data
January 2025
IBM Research, Hursley, SO21 2JN, UK.
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one of the most interesting challenges of contemporary computational chemistry. However, the traditional QM methods are costly for this application. Machine learning techniques have emerged as a powerful tool for substituting the traditional QM methods.
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