:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Hinuma, Yoyo
Format:	Preprint
Published:	2025
Subjects:	Materials Science
Online Access:	https://arxiv.org/abs/2502.02056
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural network potential molecular dynamics simulations of (La,Ce,Pr,Nd)0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)0.05F2.95
by: Hinuma, Yoyo
Published: (2025)

Effect of very slow O diffusion at high temperature on very fast H diffusion in the hydride ion conductor LaH2.75O0.125
by: Hinuma, Yoyo
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)

Thermodynamics of alkali feldspar solid solutions with varying Al-Si order: atomistic simulations using a neural network potential
by: Gorfer, Alexander, et al.
Published: (2024)

Trillion-atom molecular dynamics simulations with ab initio accuracy
by: Suo, Pengfei, et al.
Published: (2026)

Enhanced solid solution hardening by off-center substitutional solute atoms in α-Ti
by: Yu, Zi-Han, et al.
Published: (2024)

Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations
by: Romano, Salvatore, et al.
Published: (2024)

Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials
by: Nghia, Le Huu, et al.
Published: (2026)

Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
by: Ehsan, Mashaekh Tausif, et al.
Published: (2024)

Discovery of highly anisotropic dielectric crystals with equivariant graph neural networks
by: Lou, Yuchen, et al.
Published: (2024)

AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals
by: Hu, Guangyu, et al.
Published: (2024)

Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)

Advancing atomic electron tomography with neural networks
by: Lee, Juhyeok, et al.
Published: (2025)

Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
by: Song, Keke, et al.
Published: (2024)

Theoretical analysis of zirconium oxynitride/water interface using neural network potential
by: Nakanishi, Akitaka, et al.
Published: (2023)

Nanoparticle simulations of logarithmic creep and microprestress relaxation in concrete and other disordered solids
by: Masoero, Enrico, et al.
Published: (2025)

A general-purpose neural network potential for Ti-Al-Nb alloys towards large-scale molecular dynamics with ab initio accuracy
by: Zhao, Zhiqiang, et al.
Published: (2024)

Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
by: Brännvall, Marian Arale, et al.
Published: (2024)

Kolmogorov--Arnold networks in molecular dynamics
by: Nagai, Yuki, et al.
Published: (2024)

Lattice thermal conductivity of 16 elemental metals from molecular dynamics simulations with a unified neuroevolution potential
by: Cao, Shuo, et al.
Published: (2025)

Role of long-range dipolar interactions in the simulation of the properties of polar crystals using effective atomic potentials
by: Yu, Miao, et al.
Published: (2025)

Structure and thermodynamics of defects in Na-feldspar from a neural network potential
by: Gorfer, Alexander, et al.
Published: (2024)

Ab initio molecular dynamics of paramagnetic uranium mononitride (UN) using disordered local moments
by: AbdulHameed, Mohamed, et al.
Published: (2025)

Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids
by: Chuprov, Artem, et al.
Published: (2026)

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
by: Ying, Penghua, et al.
Published: (2024)

Temperature-driven structural phase transitions in SmNiO$_3$: insights from deep potential molecular dynamics simulations
by: Shi, Guoyong, et al.
Published: (2025)

The origin of strings and rings in the atomic dynamics of disordered systems
by: Hussein, Omar, et al.
Published: (2024)

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response
by: Patel, Ravi, et al.
Published: (2024)

Threshold displacement energy map of Frenkel pair generation in $\rm Ga_2O_3$ from machine-learning-driven molecular dynamics simulations
by: He, Huan, et al.
Published: (2024)

Application of canonical augmentation to the atomic substitution problem
by: Prayogo, Genki I., et al.
Published: (2021)

Virp: neural network-accelerated prediction of physical properties in site-disordered materials
by: Chen, Andy Paul, et al.
Published: (2026)

Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
by: Kasamatsu, Shusuke, et al.
Published: (2020)

Physics-aware neural networks enable robust and full atomic structure determination via low-dose atomic electron tomography
by: Zhang, Yao, et al.
Published: (2026)

Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO3 using large-scale molecular dynamics simulations based on machine learning potentials
by: Kanayama, Kansei, et al.
Published: (2024)

Leveraging neural network interatomic potentials for a foundation model of chemistry
by: Kim, So Yeon, et al.
Published: (2025)

Modeling phase separation in polymer-derived silicon carbonitride ceramics through extended machine learning molecular dynamics
by: Mortier, Fabien, et al.
Published: (2026)

Spontaneous Perovskite Passivator Featuring Dual Functional Groups
by: Naoyuki Nishimura, et al.
Published: (2026)

Molecular dynamics simulation study of mechanical properties of 3C-SiC with extended defects
by: Shmahlii, Serhii, et al.
Published: (2026)

Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
by: Kutana, Alex, et al.
Published: (2024)

Equivariant graph neural network surrogates for predicting the properties of relaxed atomic configurations
by: Holber, Jamie, et al.
Published: (2025)