Copertina

Mitigating error cancellation in density functional approximations via machine learning correction

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	An, Zipeng, Wang, JingChun, Zhang, Yapeng, Li, Zhiyu, Wu, Jiang, Zheng, Yalun, Chen, GuanHua, Zheng, Xiao
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Chemical Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

PyGSC: A Python tool for correcting Kohn-Sham orbital energies by mitigating the delocalization error of density functional approximations
di: An, Zipeng, et al.
Pubblicazione: (2026)

A foundation machine learning potential with polarizable long-range interactions for materials modelling
di: Gao, Rongzhi, et al.
Pubblicazione: (2024)

High-Energy Reaction Dynamics of N$_{3}$
di: Wang, JingChun, et al.
Pubblicazione: (2024)

olLOSC: Unified and efficient density functional approximation to correct delocalization error in molecules and periodic materials
di: Fan, Yichen, et al.
Pubblicazione: (2026)

High-Energy Reaction Dynamics of O$_3$
di: Wang, JingChun, et al.
Pubblicazione: (2025)

Why does the $GW$ approximation give accurate quasiparticle energies? The cancellation of vertex corrections quantified
di: Förster, Arno, et al.
Pubblicazione: (2024)

Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
di: Kelley, Michelle M., et al.
Pubblicazione: (2024)

Machine learning for accuracy in density functional approximations
di: Voss, Johannes
Pubblicazione: (2023)

Copula approach to exchange-correlation hole in many-electron systems with strong correlations
di: Wang, JingChun, et al.
Pubblicazione: (2022)

Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
di: Navarro-Rodríguez, Sara, et al.
Pubblicazione: (2026)

Reliable emulation of complex functionals by active learning with error control
di: Fang, Xinyi, et al.
Pubblicazione: (2022)

Double-hybrid density-functional theory with density-based basis-set correction
di: Znaïda, Aurore, et al.
Pubblicazione: (2025)

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

Global approximations to the error function of real argument for vectorized computation
di: Laikov, Dimitri N.
Pubblicazione: (2025)

Approximate normalizations for approximate density functionals
di: Clay, Adam, et al.
Pubblicazione: (2025)

Impact of the damping function in dispersion-corrected density functional theory on the properties of liquid water
di: Lausch, K. Nikolas, et al.
Pubblicazione: (2025)

Accurate estimation of polyethylene glycol density via machine‐learning based techniques
di: Farag M. A. Altalbawy, et al.
Pubblicazione: (2025)

Can machines learn density functionals? Past, present, and future of ML in DFT
di: Akashi, Ryosuke, et al.
Pubblicazione: (2025)

Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning
di: Krug, Simon León, et al.
Pubblicazione: (2024)

Enhancing molecular dynamics with equivariant machine-learned densities
di: Bogojeski, Mihail, et al.
Pubblicazione: (2026)

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
di: Wang, JingChun, et al.
Pubblicazione: (2025)

A universal machine learning model for the electronic density of states
di: How, Wei Bin, et al.
Pubblicazione: (2025)

Physics-informed machine learning for combustion: A review
di: Wu, Jiahao, et al.
Pubblicazione: (2025)

Real-space Hubbard-corrected density functional theory
di: Bhowmik, Sayan, et al.
Pubblicazione: (2025)

Adaptive energy reference for machine-learning models of the electronic density of states
di: How, Wei Bin, et al.
Pubblicazione: (2024)

Synergistic integration of physical embedding and machine learning enabling precise and reliable force field
di: Xu, Lifeng, et al.
Pubblicazione: (2024)

Does basis set superposition error significantly affect post-CCSD(T) corrections?
di: Fishman, Vladimir, et al.
Pubblicazione: (2024)

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

Local density approximation for excited states
di: Gould, Tim, et al.
Pubblicazione: (2023)

Grad DFT: a software library for machine learning enhanced density functional theory
di: Casares, Pablo A. M., et al.
Pubblicazione: (2023)

Optimized auxiliary functions for robust mitigation of finite-size errors in periodic hybrid density functional theory
di: Quiton, Stephen Jon, et al.
Pubblicazione: (2025)

A charge-density machine-learning workflow for computing the infrared spectrum of molecules
di: Hazra, Suman, et al.
Pubblicazione: (2025)

Interaction between water and carbon nanostructures: How good are current density functional approximations?
di: Brandenburg, Jan Gerit, et al.
Pubblicazione: (2019)

Estimation of density and viscosity of deep eutectic solvents: Experimental and machine learning approach
di: Dhruv Patel, et al.
Pubblicazione: (2024)

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

Uncertainty in the era of machine learning for atomistic modeling
di: Grasselli, Federico, et al.
Pubblicazione: (2025)

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

SPA$^\mathrm{H}$M(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
di: Briling, Ksenia R., et al.
Pubblicazione: (2023)

Next-order correction to the Dirac exchange energy of the free electron gas in the thermodynamic limit and generalized gradient approximations
di: Corso, Thiago Carvalho, et al.
Pubblicazione: (2023)

Constructing accurate and efficient general-purpose atomistic machine learning model with transferable accuracy for quantum chemistry
di: Chen, Yicheng, et al.
Pubblicazione: (2024)