Copertina

Thermal transport of glasses via machine learning driven simulations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pegolo, Paolo, Grasselli, Federico
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Disordered Systems and Neural Networks Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Thermal transport of amorphous hafnia across the glass transition
di: Zeng, Zezhu, et al.
Pubblicazione: (2025)

Effects of colored disorder on the heat conductivity of SiGe alloys from first principles
di: Fiorentino, Alfredo, et al.
Pubblicazione: (2024)

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation
di: Wadowski, Antoni, et al.
Pubblicazione: (2024)

Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine-learning interatomic potential vs.\ reverse Monte Carlo fitting to experimental data
di: Matsutani, Kenta, et al.
Pubblicazione: (2024)

Unveiling the crystallization kinetics in Ge-rich Ge$_x$Te alloys by large scale simulations with a machine-learned interatomic potential
di: Baratella, Dario, et al.
Pubblicazione: (2024)

On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
di: Kheir, Omar Abou El, et al.
Pubblicazione: (2026)

Hydrogen liquid-liquid transition from first principles and machine learning
di: Tenti, Giacomo, et al.
Pubblicazione: (2025)

Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
di: Choyal, Vijay, et al.
Pubblicazione: (2023)

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach
di: Mishra, Sachit, et al.
Pubblicazione: (2022)

Structural properties of amorphous Na$_3$OCl electrolyte by first-principles and machine learning molecular dynamics
di: Pham, T. -L., et al.
Pubblicazione: (2024)

Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
di: Dicks, Oliver A., et al.
Pubblicazione: (2025)

Growth and prediction of plastic strain in metallic glasses
di: Mäkinen, Tero, et al.
Pubblicazione: (2025)

Cooperative atomic motion during shear deformation in metallic glass
di: Shiihara, Yoshinori, et al.
Pubblicazione: (2025)

Relationship between the boson peak and first sharp diffraction peak in glasses
di: Kyotani, Dan, et al.
Pubblicazione: (2025)

Spin glass behavior in amorphous CrSiTe3 alloy
di: Wang, Xiaozhe, et al.
Pubblicazione: (2025)

Efficient sampling method for multi-component ZrCu(Al) metallic glasses
di: Kaskosz, Filip, et al.
Pubblicazione: (2023)

Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties
di: Mäkinen, Tero, et al.
Pubblicazione: (2024)

Unveiling the early stage evolution of local atomic structures in the crystallization process of a metallic glass
di: Ma, Lin, et al.
Pubblicazione: (2024)

Experimentally validated and empirically compared machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys from Co-Cr-Fe-Mn-Ni system
di: Chandraker, Abhinav, et al.
Pubblicazione: (2023)

Relationship between the shear modulus and volume relaxation in high-entropy metallic glasses: experiment and physical origin
di: Khmyrov, R. S., et al.
Pubblicazione: (2024)

Revealing hidden medium-range order in silicate glass-formers using many-body correlation functions
di: Zhang, Zhen, et al.
Pubblicazione: (2024)

X-ray photo-induced atomic motion in Phase Change Materials and conventional covalent chalcogenide glasses
di: Festi, Irene, et al.
Pubblicazione: (2026)

Crystal-like thermal transport in amorphous carbon
di: Moon, Jaeyun, et al.
Pubblicazione: (2024)

Iron phosphate glass structure at different length scale with emphasis on the medium range: a classical molecular dynamic study
di: Singh, Shakti, et al.
Pubblicazione: (2024)

Predicting temperature-dependent failure and transformation zones in 2D silica glass through quasistatic Gaussian Phase Packets
di: Spínola, Miguel, et al.
Pubblicazione: (2025)

Discovering new photovoltaics using optimal transport theory
di: Walker, Matthew A. H., et al.
Pubblicazione: (2026)

Non-equilibrium dynamics in geometrically frustrated spin glass Bi$_2$Fe$_3$GaO$_9$ with a Cairo lattice
di: Mihaylova, Desislava, et al.
Pubblicazione: (2024)

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
di: Yang, Kai, et al.
Pubblicazione: (2025)

Effects of Galactic Irradiation on Thermal and Electronic Transport in Tungsten
di: Ugwumadu, C., et al.
Pubblicazione: (2025)

Force field optimization by end-to-end differentiable atomistic simulation
di: Gangan, Abhijeet S., et al.
Pubblicazione: (2024)

Diagrammatic quantum Monte Carlo toward the calculation of transport properties in disordered semiconductors
di: Wang, Yu-Chen, et al.
Pubblicazione: (2022)

Atomic scale structure and dynamical properties of (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ glasses through first-principles modeling and XRD measurements
di: Shuaib, Firas, et al.
Pubblicazione: (2025)

Machine learning the local electronic density of states
di: Aryanpour, A., et al.
Pubblicazione: (2025)

Patterns of load, elastic energy and damage in network models of architected composite materials
di: Greff, Christian, et al.
Pubblicazione: (2026)

RAFFLE: Active learning accelerated interface structure prediction
di: Taylor, Ned Thaddeus, et al.
Pubblicazione: (2025)

Elastic properties of fluid mercury across the metal-nonmetal transition: Ab initio simulation study
di: Bryk, T., et al.
Pubblicazione: (2025)

Machine learning intermolecular transfer integrals with compact atomic cluster representations
di: Keeratikarn, Keerati, et al.
Pubblicazione: (2025)

Physics-informed operator learning for transferable energy-dissipative microstructure dynamics
di: Xiong, Jie, et al.
Pubblicazione: (2026)

Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
di: Valiulin, V. E., et al.
Pubblicazione: (2026)

Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications
di: Kazemi, Zahra, et al.
Pubblicazione: (2024)