Cover Image

Machine learning interatomic potential can infer electrical response

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhong, Peichen, Kim, Dongjin, King, Daniel S., Cheng, Bingqing
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning Chemical Physics Computational Physics
Online Access:	Acceder al recurso
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)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)

Learning charges and long-range interactions from energies and forces
by: Kim, Dongjin, et al.
Published: (2024)

Polarizable atomic multipoles for learning long-range electrostatics
by: Kim, Dongjin, et al.
Published: (2026)

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

Latent Ewald summation for machine learning of long-range interactions
by: Cheng, Bingqing
Published: (2024)

Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)

High-performance training and inference for deep equivariant interatomic potentials
by: Tan, Chuin Wei, et al.
Published: (2025)

Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
by: Peng, Jiayu, et al.
Published: (2026)

Ion-modulated structure, proton transfer, and capacitance in the Pt(111)/water electric double layer
by: Wang, Xiaoyu, et al.
Published: (2025)

How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)

Suitability of available interatomic potentials for Sn to model its 2D allotropes
by: Maździarz, Marcin
Published: (2024)

Machine Learning Multiscale Interactions
by: Solé, Àlex, et al.
Published: (2026)

PAL -- Parallel active learning for machine-learned potentials
by: Zhou, Chen, et al.
Published: (2024)

Machine learning frontier orbital energies of nanodiamonds
by: Kirschbaum, Thorren, et al.
Published: (2022)

Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, 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)

MBD-ML: Many-body dispersion from machine learning for molecules and materials
by: Moerman, Evgeny, et al.
Published: (2026)

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
by: Holzenkamp, Matthias, et al.
Published: (2024)

Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)

Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)

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)

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, et al.
Published: (2024)

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)

Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
by: Ibragimova, Rina, et al.
Published: (2024)

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
by: Zaverkin, Viktor, et al.
Published: (2023)

Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
by: Tiefenbacher, Maximilian X., et al.
Published: (2025)

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
by: Meyer, Ralf, 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)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)

Considerations in the use of ML interaction potentials for free energy calculations
by: Mendible, Orlando A., et al.
Published: (2024)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
by: Matin, Sakib, et al.
Published: (2025)

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials
by: Mahmoud, Chiheb Ben, et al.
Published: (2025)

Reciprocal Space Attention for Learning Long-Range Interactions
by: Ramasubramanian, Hariharan, et al.
Published: (2025)

Two-dimensional RMSD projections for reaction path visualization and validation
by: Goswami, Rohit
Published: (2025)

Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
by: Thiemann, Fabian L., et al.
Published: (2025)