Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 19 min ago
PreprintarXiv licencearXiv

Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies

  • Arghadwip Paul
  • Bikash Kanungo
  • Sambit Das
  • Vikram Gavini
Open at sourcePDFDOICite

Abstract

We present NNLap, a machine-learned Laplacian-level exchange-correlation (XC) functional that augments PBE with a neural-network correction depending on the electron density, its gradient, and its Laplacian. The model is trained on exact XC potentials and energies, obtained through inverse density-functional theory (DFT) calculations on configuration-interaction densities. Despite training on only a few systems -- five atoms and three molecules -- the model achieves remarkable accuracy on thermochemistry benchmarks, competing with the meta-GGA functionals SCAN and r2SCAN. It also attains accurate total energies, comparable to SCAN and better than r2SCAN and B3LYP. This shows that a Laplacian-level model, trained on exact XC potentials and energies, can reach the accuracy of meta-GGAs without their orbital dependence.

Related

  1. Exchange-Correlation Potentials and Energies from Inverse Generalized Kohn-Sham Calculations
    Vishal Subramanian, Bikash Kanungo, Vaibhav Khanna +2
    arXivPreprintCC BY 4.0Full textPhysical chemistryComputational physicsarXiv
  2. Nonempirical Time-Dependent Density Functional Theory Framework for Nonlocal Exchange--Correlation Potentials
    Zhandos A. Moldabekov, Michele Pavanello, Thomas D. Gawne +2
    arXivPreprintarXiv licenceFull textPhysical chemistryCondensed matterarXiv
  3. Influence of exchange–correlation functional on machine-learnt interatomic potentials accuracy: a systematic study in borosilicate glasses
    Fengming Shi, Luca Brugnoli, François-Xavier Coudert
    ChemRxivPreprintCC BYMaterials & polymersChemRxiv
  4. Influence of Exchange–Correlation Functional on Machine-Learned Interatomic Potentials’ Accuracy: A Systematic Study of Borosilicate Glasses
    Fengming Shi, Luca Brugnoli, François-Xavier Coudert
    Journal of Chemical Theory and ComputationPeer-reviewedCC BY 4.0Full textMaterials & polymersTheoretical & computational chemistryHAL
  5. Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals
    Yuhao Chen, Donald G. Truhlar, Xiao He
    arXivPreprintarXiv licenceFull textPhysical chemistryarXiv

Cite

BibTeXRIS
BibTeX
@misc{paul2026machinelearned,
  title = {{Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies}},
  author = {Arghadwip Paul and Bikash Kanungo and Sambit Das and Vikram Gavini},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.34194},
  eprint = {2609.34194},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.34194},
}
RIS
TY  - UNPB
TI  - Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies
AU  - Arghadwip Paul
AU  - Bikash Kanungo
AU  - Sambit Das
AU  - Vikram Gavini
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.34194
UR  - https://arxiv.org/abs/2609.34194
AB  - We present NNLap, a machine-learned Laplacian-level exchange-correlation (XC) functional that augments PBE with a neural-network correction depending on the electron density, its gradient, and its Laplacian. The model is trained on exact XC potentials and energies, obtained through inverse density-functional theory (DFT) calculations on configuration-interaction densities. Despite training on only a few systems -- five atoms and three molecules -- the model achieves remarkable accuracy on thermochemistry benchmarks, competing with the meta-GGA functionals SCAN and r2SCAN. It also attains accurate total energies, comparable to SCAN and better than r2SCAN and B3LYP. This shows that a Laplacian-level model, trained on exact XC potentials and energies, can reach the accuracy of meta-GGAs without their orbital dependence.
ER  -