Latent Ewald summation for machine learning of long-range interactions
Fuente:
arXiv
Saved in:
| Main Author: | Cheng, Bingqing |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Polarizable atomic multipoles for learning long-range electrostatics
by: Kim, Dongjin, et al.
Published: (2026)
by: Kim, Dongjin, et al.
Published: (2026)
Model density approach to Ewald summations
by: Ribaldone, Chiara, et al.
Published: (2026)
by: Ribaldone, Chiara, et al.
Published: (2026)
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Cartesian atomic cluster expansion for machine learning interatomic potentials
by: Cheng, Bingqing
Published: (2024)
by: Cheng, Bingqing
Published: (2024)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Learning charges and long-range interactions from energies and forces
by: Kim, Dongjin, et al.
Published: (2024)
by: Kim, Dongjin, et al.
Published: (2024)
MBD-ML: Many-body dispersion from machine learning for molecules and materials
by: Moerman, Evgeny, et al.
Published: (2026)
by: Moerman, Evgeny, et al.
Published: (2026)
A foundation machine learning potential with polarizable long-range interactions for materials modelling
by: Gao, Rongzhi, et al.
Published: (2024)
by: Gao, Rongzhi, et al.
Published: (2024)
Delta-learned force fields for nonbonded interactions: Addressing the strength mismatch between covalent-nonbonded interaction for global models
by: Cázares-Trejo, Leonardo, et al.
Published: (2025)
by: Cázares-Trejo, Leonardo, 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)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Reducing cross-sample prediction churn in scientific machine learning
by: Prastalo, Gordan, et al.
Published: (2026)
by: Prastalo, Gordan, et al.
Published: (2026)
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)
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., et al.
Published: (2025)
Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)
by: Zhang, Tianren, et al.
Published: (2025)
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)
Grad DFT: a software library for machine learning enhanced density functional theory
by: Casares, Pablo A. M., et al.
Published: (2023)
by: Casares, Pablo A. M., et al.
Published: (2023)
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, et al.
Published: (2024)
by: Liu, Junlan, et al.
Published: (2024)
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., et al.
Published: (2025)
Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
by: Qu, Eric, et al.
Published: (2026)
by: Qu, Eric, et al.
Published: (2026)
Considerations in the use of ML interaction potentials for free energy calculations
by: Mendible, Orlando A., et al.
Published: (2024)
by: Mendible, Orlando A., et al.
Published: (2024)
Machine learning frontier orbital energies of nanodiamonds
by: Kirschbaum, Thorren, et al.
Published: (2022)
by: Kirschbaum, Thorren, et al.
Published: (2022)
Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)
by: Uhrin, Martin, et al.
Published: (2024)
LATTE: an atomic environment descriptor based on Cartesian tensor contractions
by: Pellegrini, Franco, et al.
Published: (2024)
by: Pellegrini, Franco, et al.
Published: (2024)
Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
by: Kanungo, Bikash, et al.
Published: (2024)
by: Kanungo, Bikash, et al.
Published: (2024)
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)
by: Solé, Àlex, et al.
Published: (2025)
Reciprocal Space Attention for Learning Long-Range Interactions
by: Ramasubramanian, Hariharan, et al.
Published: (2025)
by: Ramasubramanian, Hariharan, et al.
Published: (2025)
Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
by: Ullberg, R. Seaton, et al.
Published: (2026)
by: Ullberg, R. Seaton, et al.
Published: (2026)
Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment
by: Goswami, Rohit, et al.
Published: (2026)
by: Goswami, Rohit, et al.
Published: (2026)
CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
by: Zhang, Chengqian, et al.
Published: (2026)
by: Zhang, Chengqian, et al.
Published: (2026)
Geometric Deep Learning for Molecular Crystal Structure Prediction
by: Kilgour, Michael, et al.
Published: (2023)
by: Kilgour, Michael, et al.
Published: (2023)
Two-dimensional RMSD projections for reaction path visualization and validation
by: Goswami, Rohit
Published: (2025)
by: Goswami, Rohit
Published: (2025)
Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
by: Thiemann, Fabian L., et al.
Published: (2025)
by: Thiemann, Fabian L., et al.
Published: (2025)
Coupled reaction and diffusion governing interface evolution in solid-state batteries
by: Ding, Jingxuan, et al.
Published: (2025)
by: Ding, Jingxuan, et al.
Published: (2025)
Machine Learning Multiscale Interactions
by: Solé, Àlex, et al.
Published: (2026)
by: Solé, Àlex, et al.
Published: (2026)
Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
by: Goswami, Rohit, et al.
Published: (2025)
by: Goswami, Rohit, et al.
Published: (2025)
Similar Items
-
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025) -
Polarizable atomic multipoles for learning long-range electrostatics
by: Kim, Dongjin, et al.
Published: (2026) -
Model density approach to Ewald summations
by: Ribaldone, Chiara, et al.
Published: (2026) -
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025) -
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)