Image de couverture de livre

On the chemical potential and grand potential density of solids under non-hydrostatic stress

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Sprik, Michiel
Format:	Preprint
Publié:	2024
Sujets:	Materials Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Thermodynamics of a compressible lattice gas crystal: Generalized Gibbs-Duhem equation and adsorption
par: Sprik, Michiel
Publié: (2025)

Machine learning interatomic potentials for solid-state precipitation
par: Piersante, Lorenzo, et autres
Publié: (2026)

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
par: Liu, Xiaoqing, et autres
Publié: (2026)

Regional chemical potential analysis for material surfaces
par: Fukuda, Masahiro, et autres
Publié: (2025)

Effect of hydrostatic pressure and alloying on thermoelectric properties of van der Waals solid KMgSb: An \textit{ab-initio} study
par: Chaudhary, Vikrant, et autres
Publié: (2023)

Effect of particle size on the phase transformation behavior and equation of state of Si under hydrostatic loading
par: Yesudhas, Sorb, et autres
Publié: (2024)

Effects of mechanical stress, chemical potential, and coverage on hydrogen solubility during hydrogen-enhanced decohesion of ferritic steel grain boundaries: A first-principles study
par: Guzmán, Abril Azócar, et autres
Publié: (2024)

Nonequilibrium thermodynamic foundation of the grand-potential phase field model
par: Zhang, Jin, et autres
Publié: (2024)

Predicting ionic conductivity in solids from the machine-learned potential energy landscape
par: Maevskiy, Artem, et autres
Publié: (2024)

Break-down of the relationship between α-relaxation and equilibration in hydrostatically compressed metallic glasses
par: Cornet, Antoine, et autres
Publié: (2024)

Transformations induced by hydrostatic pressure on lead metasilicate phases
par: Rodrigues, Ariano D., et autres
Publié: (2025)

Effect of initial microstructure on its evolution and $α\rightarrow ω$ phase transition in Zr under hydrostatic loading
par: Pandey, K. K., et autres
Publié: (2023)

Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials
par: Xu, Xiang, et autres
Publié: (2024)

Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity
par: Koutná, Nikola, et autres
Publié: (2025)

A non-local exchange potential for electronic structure calculations
par: Schiwietz, G., et autres
Publié: (2025)

Interatomic potentials for platinum
par: Koju, R. K., et autres
Publié: (2026)

Coupled phase transitions in crystalline solids with extreme chemical disorder
par: Dey, Subha, et autres
Publié: (2026)

Exploring the potential of combining over- and under-stoichiometric MIEC materials for Oxygen-Ion Batteries
par: Panisset, Silvère, et autres
Publié: (2024)

On the role played by electrons in the stress-strain curves of ideal crystalline solids
par: Marsili, Margherita, et autres
Publié: (2025)

Observation of hydrostatic-pressure-modulated giant caloric effect and electronic topological transition
par: Yang, Jinying, et autres
Publié: (2024)

Large bulk photovoltaic effect and Fermi surface mediated its enhancement with chemical potential in ZnGeP$_2$
par: Sadhukhan, Banasree
Publié: (2024)

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials
par: Ou, Yongliang, et autres
Publié: (2024)

Atom probe tomography: a local probe for chemical bonds in solids
par: Cojocaru-Mirédin, Oana, et autres
Publié: (2024)

Real-space methods for ab initio modelling of surfaces and interfaces under external potential bias
par: Ramakrishnan, Kartick, et autres
Publié: (2025)

Robust spin-orbit coupling in semi-metallic SrIrO3 under hydrostatic pressure
par: Fuchs, Dirk, et autres
Publié: (2025)

VO$_2$ under hydrostatic pressure: Isostructural phase transition close to a critical end-point
par: Bouvier, P., et autres
Publié: (2023)

Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials
par: Yan, Zihan, et autres
Publié: (2025)

Foundations of the ionization potential condition for localized electron removal in density functional theory
par: Ohad, Guy, et autres
Publié: (2025)

Direct derivation of anisotropic atomic displacement parameters from molecular dynamics simulations in extended solids with substitutional disorder using a neural network potential
par: Hinuma, Yoyo
Publié: (2025)

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
par: Kang, Hanwen, et autres
Publié: (2025)

Assessing the potential of perfect screw dislocations in SiC for solid-state quantum technologies
par: Barragan-Yani, Daniel, et autres
Publié: (2023)

Environment-adaptive machine learning potentials
par: Nguyen, Ngoc Cuong, et autres
Publié: (2024)

Analytical model for the remote epitaxial potential
par: Kawasaki, Jason K, et autres
Publié: (2025)

Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
par: Ibragimova, Rina, et autres
Publié: (2024)

Spin migration in density functional theory: energy, potential and density perspectives
par: Hayman, Alon, et autres
Publié: (2024)

First-principles study of structural, electronic and optical properties of non-toxic RbBaX$_3$ (X = F, Cl, Br, I) perovskites under hydrostatic pressure
par: Saha, Pranti, et autres
Publié: (2024)

Designing explicit functionals for the charge density in terms of a potential
par: Güneş, Muhammed Hüseyin, et autres
Publié: (2026)

Machine learning potential for the Cu-W system
par: Liyanage, Manura, et autres
Publié: (2024)

Topological descriptors for the electron density of inorganic solids
par: Szymanski, Nathan J., et autres
Publié: (2025)

Machine learning Landau free energy potentials
par: Pulzone, Mauro, et autres
Publié: (2025)