Cover Image

A foundation machine learning potential with polarizable long-range interactions for materials modelling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Rongzhi, Yam, ChiYung, Mao, Jianjun, Chen, Shuguang, Chen, GuanHua, Hu, Ziyang
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mitigating error cancellation in density functional approximations via machine learning correction
by: An, Zipeng, et al.
Published: (2025)

Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning
by: Han, Mingjun, et al.
Published: (2024)

Accurate molecular polarizabilities with coupled-cluster theory and machine learning
by: Wilkins, David M., et al.
Published: (2018)

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
by: Zaverkin, Viktor, et al.
Published: (2025)

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

Fast and flexible long-range models for atomistic machine learning
by: Loche, Philip, et al.
Published: (2024)

An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
by: Zhang, Lingfeng, et al.
Published: (2026)

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

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

On the performance of QTP functionals applied to second-order response properties II: Dynamic polarizability and long-range C$_6$ coefficients
by: Mendes, Rodrigo A., et al.
Published: (2026)

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
by: de Kam, Lucas B. T., et al.
Published: (2026)

Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2025)

Electron transport properties of heterogeneous interfaces in solid electrolyte interphase on lithium metal anodes
by: Zhou, Xiangyi, et al.
Published: (2025)

Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials
by: Chen, Yicheng, et al.
Published: (2025)

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
by: Li, Xinze, et al.
Published: (2026)

Introduction to machine learning potentials for atomistic simulations
by: Thiemann, Fabian L., et al.
Published: (2024)

Diatomic molecules of alkali-metal and alkaline-earth-metal atoms: interaction potentials, dipole moments, and polarizabilities
by: Ladjimi, Hela, et al.
Published: (2023)

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

Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
by: Ge, Fuchun, et al.
Published: (2024)

Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations
by: Yuan, Eric C. -Y., et al.
Published: (2025)

Efficient polarizable QM/MM using the direct reaction field Hamiltonian with electrostatic potential fitted multipole operators
by: Fay, Thomas P., et al.
Published: (2024)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
by: Stolte, Nore, et al.
Published: (2024)

All-in-one foundational models learning across quantum chemical levels
by: Chen, Yuxinxin, 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)

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
by: Vorotnikov, Igor, et al.
Published: (2025)

A foundation model for atomistic materials chemistry
by: Batatia, Ilyes, et al.
Published: (2023)

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

Global properties of the energy landscape: a testing and training arena for machine learned potentials
by: Cărare, Vlad, et al.
Published: (2025)

Thawed Gaussian wavepacket dynamics with $Δ$-machine learned potentials
by: Gherib, Rami, et al.
Published: (2024)

Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
by: Jinnouchi, Ryosuke, et al.
Published: (2024)

Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
by: Brugnoli, Luca, et al.
Published: (2026)

Performance of range-separated long-range SOPPA short-range density functional theory method for vertical excitation energies
by: Fuglsbjerg, Juliane H., et al.
Published: (2024)

Accurate nuclear quantum statistics on machine-learned classical effective potentials
by: Zaporozhets, Iryna, et al.
Published: (2024)

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

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2026)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)

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