Machine-learning force-field models for dynamical simulations of metallic magnets
Fuente:
arXiv
Guardado en:
| Autores principales: | Chern, Gia-Wei, Fan, Yunhao, Zhang, Sheng, Zhang, Puhan |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
por: Chern, Gia-Wei, et al.
Publicado: (2026)
por: Chern, Gia-Wei, et al.
Publicado: (2026)
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
por: Fan, Yunhao, et al.
Publicado: (2026)
por: Fan, Yunhao, et al.
Publicado: (2026)
Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach
por: Fan, Yunhao, et al.
Publicado: (2024)
por: Fan, Yunhao, et al.
Publicado: (2024)
Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets
por: Tyberg, Alexa, et al.
Publicado: (2024)
por: Tyberg, Alexa, et al.
Publicado: (2024)
Machine learning nonequilibrium phase transitions in charge-density wave insulators
por: Fan, Yunhao, et al.
Publicado: (2026)
por: Fan, Yunhao, et al.
Publicado: (2026)
Machine Learning Force-Field Approach for Itinerant Electron Magnets
por: Zhang, Sheng, et al.
Publicado: (2025)
por: Zhang, Sheng, et al.
Publicado: (2025)
Equivariant Neural Networks for Force-Field Models of Lattice Systems
por: Fan, Yunhao, et al.
Publicado: (2026)
por: Fan, Yunhao, et al.
Publicado: (2026)
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
por: Yang, Yang, et al.
Publicado: (2024)
por: Yang, Yang, et al.
Publicado: (2024)
Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
por: Ghosh, Supriyo, et al.
Publicado: (2024)
por: Ghosh, Supriyo, et al.
Publicado: (2024)
Echo State network for coarsening dynamics of charge density waves
por: Dinh, Clement, et al.
Publicado: (2024)
por: Dinh, Clement, et al.
Publicado: (2024)
Transformer Learning of Chaotic Collective Dynamics in Many-Body Systems
por: Jang, Ho, et al.
Publicado: (2026)
por: Jang, Ho, et al.
Publicado: (2026)
Kinetics of Peierls dimerization transition: Machine learning force-field approach
por: Jang, Ho, et al.
Publicado: (2025)
por: Jang, Ho, et al.
Publicado: (2025)
Nonequilibrium Dynamics of Gating-Induced Resistance Transition in Charge Density Wave Insulators
por: Zhang, Sheng, et al.
Publicado: (2022)
por: Zhang, Sheng, et al.
Publicado: (2022)
Machine Learning Modeling of Charge-Density-Wave Recovery After Laser Melting
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
Learning Degenerate Manifolds of Frustrated Magnets with Boltzmann Machines
por: Jang, Ho, et al.
Publicado: (2025)
por: Jang, Ho, et al.
Publicado: (2025)
Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
por: Rayat, Ali, et al.
Publicado: (2026)
por: Rayat, Ali, et al.
Publicado: (2026)
Convolutional neural networks for large-scale dynamical modeling of itinerant magnets
por: Cheng, Xinlun, et al.
Publicado: (2023)
por: Cheng, Xinlun, et al.
Publicado: (2023)
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
por: Rayat, Ali, et al.
Publicado: (2026)
por: Rayat, Ali, et al.
Publicado: (2026)
Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics
por: Peng, Changnan, et al.
Publicado: (2024)
por: Peng, Changnan, et al.
Publicado: (2024)
Emergent Spatial Textures from Interaction Quenches in the Hubbard Model
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
Scalable quantum dynamics compilation via quantum machine learning
por: Zhang, Yuxuan, et al.
Publicado: (2024)
por: Zhang, Yuxuan, et al.
Publicado: (2024)
Suppressed coarsening after an interaction quench in the Holstein chain
por: Jang, Ho, et al.
Publicado: (2026)
por: Jang, Ho, et al.
Publicado: (2026)
Photo-induced pattern formations and melting of charge-density-wave order
por: Yang, Lingyu, et al.
Publicado: (2024)
por: Yang, Lingyu, et al.
Publicado: (2024)
Pattern formation in charge density wave states after a quantum quench
por: Yang, Lingyu, et al.
Publicado: (2023)
por: Yang, Lingyu, et al.
Publicado: (2023)
Machine learning discovery of new phases in programmable quantum simulator snapshots
por: Miles, Cole, et al.
Publicado: (2021)
por: Miles, Cole, et al.
Publicado: (2021)
Anomalous coarsening and nonlinear diffusion of kinks in an one-dimensional quasi-classical Holstein model
por: Jang, Ho, et al.
Publicado: (2025)
por: Jang, Ho, et al.
Publicado: (2025)
Topological phase diagrams of in-plane field polarized Kitaev magnets
por: Chern, Li Ern, et al.
Publicado: (2023)
por: Chern, Li Ern, et al.
Publicado: (2023)
Machine learning assisted derivation of effective low energy models for metallic magnets
por: Sharma, Vikram, et al.
Publicado: (2022)
por: Sharma, Vikram, et al.
Publicado: (2022)
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout
por: Chen, Hao, et al.
Publicado: (2023)
por: Chen, Hao, et al.
Publicado: (2023)
From higher-order moments to time correlation functions in strongly correlated systems: A DMRG-based memory kernel coupling theory
por: Liu, Yunhao, et al.
Publicado: (2025)
por: Liu, Yunhao, et al.
Publicado: (2025)
Electronic frictional effects near metal surfaces with strong correlations
por: Liu, Yunhao, et al.
Publicado: (2025)
por: Liu, Yunhao, et al.
Publicado: (2025)
Coarsening dynamics of Ising-nematic order in a frustrated Heisenberg antiferromagnet
por: Yang, Yang, et al.
Publicado: (2024)
por: Yang, Yang, et al.
Publicado: (2024)
Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
por: Qiao, Bei, et al.
Publicado: (2026)
por: Qiao, Bei, et al.
Publicado: (2026)
Deep Variational Free Energy Calculation of Hydrogen Hugoniot
por: Li, Zihang, et al.
Publicado: (2025)
por: Li, Zihang, et al.
Publicado: (2025)
Generalized Lanczos method for systematic optimization of neural-network quantum states
por: Wang, Jia-Qi, et al.
Publicado: (2025)
por: Wang, Jia-Qi, et al.
Publicado: (2025)
Auxiliary dynamical mean-field approach for Anderson-Hubbard model with off-diagonal disorder
por: Zhang, Zelei, et al.
Publicado: (2025)
por: Zhang, Zelei, et al.
Publicado: (2025)
Pseudospin Formulation of Quench Dynamics in the Semiclassical Holstein Model
por: Yang, Lingyu, et al.
Publicado: (2026)
por: Yang, Lingyu, et al.
Publicado: (2026)
Schwinger-Boson Mean-Field Study of the Anisotropic Kagome Antiferromagnet
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
por: Bakshi, Sankha Subhra, et al.
Publicado: (2026)
High pressure hydrogen by machine learning and quantum Monte Carlo
por: Tirelli, Andrea, et al.
Publicado: (2021)
por: Tirelli, Andrea, et al.
Publicado: (2021)
Ground state phases of the two-dimension electron gas with a unified variational approach
por: Smith, Conor, et al.
Publicado: (2024)
por: Smith, Conor, et al.
Publicado: (2024)
Ejemplares similares
-
Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
por: Chern, Gia-Wei, et al.
Publicado: (2026) -
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
por: Fan, Yunhao, et al.
Publicado: (2026) -
Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach
por: Fan, Yunhao, et al.
Publicado: (2024) -
Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets
por: Tyberg, Alexa, et al.
Publicado: (2024) -
Machine learning nonequilibrium phase transitions in charge-density wave insulators
por: Fan, Yunhao, et al.
Publicado: (2026)