Saved in:
| Main Authors: | Galtsov, I. S., Muratov, R. V., Vyskvarko, G. V., Murzov, S. A., Dyachkov, S. A., Levashov, P. R. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.22951 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Extraordinary manifestation of near electrostatic field caused by macroscopic quantum shell effects in submicron hemispherical clusters
by: Kuratov, S. E., et al.
Published: (2024)
by: Kuratov, S. E., et al.
Published: (2024)
Phase space path integral representation of the dynamic structure factor. Monte Carlo simulation of strongly correlated soft-sphere fermions
by: Filinov, V. S., et al.
Published: (2024)
by: Filinov, V. S., et al.
Published: (2024)
Modeling of shock wave passage through porous copper using moving window technique and kernel gradient correction in smoothed particle hydrodynamics method
by: Rublev, G. D., et al.
Published: (2024)
by: Rublev, G. D., et al.
Published: (2024)
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
by: Timmerman, Lucas R., et al.
Published: (2024)
by: Timmerman, Lucas R., et al.
Published: (2024)
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
by: Solovykh, A. A., et al.
Published: (2025)
by: Solovykh, A. A., et al.
Published: (2025)
Basic stability tests of machine learning potentials for molecular simulations in computational drug discovery
by: Ranasinghe, Kavindri, et al.
Published: (2025)
by: Ranasinghe, Kavindri, et al.
Published: (2025)
N‐convergence in one–component plasma: Comparison of Coulomb, Ewald, and angular–averaged Ewald potentials
by: G. S. Demyanov, et al.
Published: (2024)
by: G. S. Demyanov, et al.
Published: (2024)
Molecular dynamics of nondegenerate hydrogen plasma using improved Kelbg pseudopotential with electron finite-size correction
by: Demyanov, G. S., et al.
Published: (2025)
by: Demyanov, G. S., et al.
Published: (2025)
Fluctuation relations to calculate protein redox potentials from molecular dynamics simulations
by: Oliveira, A. S. F., et al.
Published: (2023)
by: Oliveira, A. S. F., et al.
Published: (2023)
Adjusting the numerical viscosity in the Godunov-like SPH method at modeling compressible flows
by: Parshikov, A. N., et al.
Published: (2023)
by: Parshikov, A. N., et al.
Published: (2023)
Ultra-fast interpretable machine-learning potentials
by: Xie, Stephen R., et al.
Published: (2021)
by: Xie, Stephen R., et al.
Published: (2021)
Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
by: CampBell, Audrey, et al.
Published: (2024)
by: CampBell, Audrey, et al.
Published: (2024)
One--Component Plasma Equation of State Revisited via Angular--Averaged Ewald Potential
by: Demyanov, G. S., et al.
Published: (2025)
by: Demyanov, G. S., et al.
Published: (2025)
Understanding solid nitrogen through machine learning simulation
by: Kirsz, Marcin, et al.
Published: (2024)
by: Kirsz, Marcin, et al.
Published: (2024)
Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
by: Dong, Haikuan, et al.
Published: (2024)
by: Dong, Haikuan, et al.
Published: (2024)
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
by: Lastre, Manuel González, et al.
Published: (2026)
by: Lastre, Manuel González, et al.
Published: (2026)
Tracking electron capture processes in classical molecular dynamics simulations for spectral line broadening in plasmas
by: González-Herrero, D., et al.
Published: (2025)
by: González-Herrero, D., et al.
Published: (2025)
Generating new coordination compounds via multireference simulations, genetic algorithms and machine learning: the case of Co(II) molecular magnets
by: Frangoulis, Lion, et al.
Published: (2025)
by: Frangoulis, Lion, et al.
Published: (2025)
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
by: Wang, Menghang, et al.
Published: (2025)
by: Wang, Menghang, et al.
Published: (2025)
RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit
by: Gao, Weihang, et al.
Published: (2024)
by: Gao, Weihang, et al.
Published: (2024)
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
by: Della Pia, Flaviano, et al.
Published: (2025)
by: Della Pia, Flaviano, et al.
Published: (2025)
The transformative capability of quantum-accurate machine learning interatomic potentials
by: Correa, Alfredo A., et al.
Published: (2025)
by: Correa, Alfredo A., et al.
Published: (2025)
MicroMagnetic.jl: A Julia package for micromagnetic and atomistic simulations with GPU support
by: Wang, Weiwei, et al.
Published: (2024)
by: Wang, Weiwei, et al.
Published: (2024)
Coarse-graining bistability with the Martini force field
by: Muratov, Alexander D., et al.
Published: (2024)
by: Muratov, Alexander D., et al.
Published: (2024)
Structure and dynamics of the magnetite(001)/water interface from molecular dynamics simulations based on a neural network potential
by: Romano, Salvatore, et al.
Published: (2024)
by: Romano, Salvatore, et al.
Published: (2024)
Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
by: Zaverkin, Viktor, et al.
Published: (2025)
by: Zaverkin, Viktor, et al.
Published: (2025)
Recognizing and generating knotted molecular structures by machine learning
by: Zhang, Zhiyu, et al.
Published: (2025)
by: Zhang, Zhiyu, et al.
Published: (2025)
PAL -- Parallel active learning for machine-learned potentials
by: Zhou, Chen, et al.
Published: (2024)
by: Zhou, Chen, et al.
Published: (2024)
Accelerating fourth-generation machine learning potentials by quasi-linear scaling particle mesh charge equilibration
by: Gubler, Moritz, et al.
Published: (2024)
by: Gubler, Moritz, et al.
Published: (2024)
Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
by: Valiulin, V. E., et al.
Published: (2026)
by: Valiulin, V. E., et al.
Published: (2026)
Accelerating point defect simulations using data-driven and machine learning approaches
by: Mannodi-Kanakkithodi, Arun, et al.
Published: (2026)
by: Mannodi-Kanakkithodi, Arun, et al.
Published: (2026)
QMol-grid: A MATLAB package for quantum-mechanical simulations in atomic and molecular systems
by: Mauger, Francois, et al.
Published: (2024)
by: Mauger, Francois, et al.
Published: (2024)
Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants
by: Korogod, Dmitry, et al.
Published: (2026)
by: Korogod, Dmitry, et al.
Published: (2026)
Martini 3 application for the design of bistable nanomachines
by: Muratov, Alexander D., et al.
Published: (2025)
by: Muratov, Alexander D., et al.
Published: (2025)
Enhancing molecular dynamics with equivariant machine-learned densities
by: Bogojeski, Mihail, et al.
Published: (2026)
by: Bogojeski, Mihail, et al.
Published: (2026)
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
by: Fuchs, Paul, et al.
Published: (2025)
by: Fuchs, Paul, et al.
Published: (2025)
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
by: Zaverkin, Viktor, et al.
Published: (2023)
by: Zaverkin, Viktor, et al.
Published: (2023)
Quantum-centric machine learning for molecular dynamics
by: Tao, Yanxian, et al.
Published: (2025)
by: Tao, Yanxian, et al.
Published: (2025)
Thermometry of simulated Bose--Einstein condensates using machine learning
by: Griffiths, Jack, et al.
Published: (2025)
by: Griffiths, Jack, et al.
Published: (2025)
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
by: Fellman, Aslak, et al.
Published: (2024)
by: Fellman, Aslak, et al.
Published: (2024)
Similar Items
-
Extraordinary manifestation of near electrostatic field caused by macroscopic quantum shell effects in submicron hemispherical clusters
by: Kuratov, S. E., et al.
Published: (2024) -
Phase space path integral representation of the dynamic structure factor. Monte Carlo simulation of strongly correlated soft-sphere fermions
by: Filinov, V. S., et al.
Published: (2024) -
Modeling of shock wave passage through porous copper using moving window technique and kernel gradient correction in smoothed particle hydrodynamics method
by: Rublev, G. D., et al.
Published: (2024) -
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
by: Timmerman, Lucas R., et al.
Published: (2024) -
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
by: Solovykh, A. A., et al.
Published: (2025)