Image de couverture de livre

Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Hložný, Adam, Brndiar, Ján, Luo, Ye, Štich, Ivan
Format:	Preprint
Publié:	2026
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

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
par: Flötotto, Aaron, et autres
Publié: (2025)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
par: Matin, Sakib, et autres
Publié: (2025)

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
par: Goswami, Shubhang, et autres
Publié: (2024)

Spin-defect characteristics of single sulfur vacancies in monolayer $\text{MoS}_2$
par: Hötger, Alexander, et autres
Publié: (2022)

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
par: Gumber, Shriya, et autres
Publié: (2025)

Hessian-informed machine learning interatomic potential towards bridging theory and experiments
par: Yin, Bangchen, et autres
Publié: (2026)

Symmetry breaking and spin-orbit coupling for individual vacancy-induced in-gap states in MoS2 monolayers
par: Aliyar, Thasneem, et autres
Publié: (2024)

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
par: Baghishov, Ilgar, et autres
Publié: (2025)

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
par: Kim, Jaesun, et autres
Publié: (2024)

Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
par: Kim, Minwoo, et autres
Publié: (2026)

The structure and migration of twin boundaries in tetragonal $β$-Sn: an application of machine learning based interatomic potentials
par: Chesser, Ian, et autres
Publié: (2025)

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
par: Shuang, Fei, et autres
Publié: (2025)

Knowing when to trust machine-learned interatomic potentials
par: Mehdi, Shams, et autres
Publié: (2026)

Platonic representation of foundation machine learning interatomic potentials
par: Li, Zhenzhu, et autres
Publié: (2025)

Cartesian atomic moment machine learning interatomic potentials
par: Wen, Mingjian, et autres
Publié: (2024)

Pushing the limits of unconstrained machine-learned interatomic potentials
par: Bigi, Filippo, et autres
Publié: (2026)

Benchmarking phonon anharmonicity in machine learning interatomic potentials
par: Bandi, Sasaank, et autres
Publié: (2024)

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
par: Vorotnikov, Igor, et autres
Publié: (2025)

Comparing the latent features of universal machine-learning interatomic potentials
par: Chorna, Sofiia, et autres
Publié: (2025)

The transformative capability of quantum-accurate machine learning interatomic potentials
par: Correa, Alfredo A., et autres
Publié: (2025)

Self-consistent Coulomb interactions for machine learning interatomic potentials
par: Thomas, Jack, et autres
Publié: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
par: Cheng, Bingqing
Publié: (2024)

Lightweight equivariant model for efficient machine learning interatomic potentials
par: Yang, Ziduo, et autres
Publié: (2023)

Toward machine learning interatomic potentials for modeling uranium mononitride
par: Alzate-Vargas, Lorena, et autres
Publié: (2024)

Cross-functional transferability in universal machine learning interatomic potentials
par: Huang, Xu, et autres
Publié: (2025)

Systematic assessment of various universal machine‐learning interatomic potentials
par: Haochen Yu, et autres
Publié: (2024)

Systematic assessment of various universal machine-learning interatomic potentials
par: Yu, Haochen, et autres
Publié: (2024)

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
par: Bhatia, Nitik, et autres
Publié: (2026)

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
par: Noordhoek, Kyle, et autres
Publié: (2023)

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
par: Cho, Hyunsung, et autres
Publié: (2025)

Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors
par: Hložný, Adam, et autres
Publié: (2026)

MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
par: Lastre, Manuel González, et autres
Publié: (2026)

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
par: David, Rolf, et autres
Publié: (2024)

Exploring the energy landscape of aluminas through machine learning interatomic potential
par: Zhang, Lei, et autres
Publié: (2024)

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
par: Nam, Juno, et autres
Publié: (2024)

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
par: Georgaras, Johnathan D., et autres
Publié: (2025)

Screening of material defects using universal machine-learning interatomic potentials
par: Berger, Ethan, et autres
Publié: (2025)

Graph atomic cluster expansion for foundational machine learning interatomic potentials
par: Lysogorskiy, Yury, et autres
Publié: (2025)

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
par: Byggmästar, Jesper, et autres
Publié: (2026)

A practical guide to machine learning interatomic potentials -- Status and future
par: Jacobs, Ryan, et autres
Publié: (2025)