{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34194",
  "author_total": 4,
  "authors": [
    {
      "name": "Arghadwip Paul"
    },
    {
      "name": "Bikash Kanungo"
    },
    {
      "name": "Sambit Das"
    },
    {
      "name": "Vikram Gavini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e5g2xjqj1y1p7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34194",
  "fulltext_url": null,
  "id": "e5g2xjqj1y1p7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34194",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7214719668",
  "pdf_url": "https://arxiv.org/pdf/2609.34194",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e5g2xjqj1y1p7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.34194",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34194"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies",
  "title_text": "Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/e5g2xjqj1y1p7",
  "venue": "arXiv",
  "versions": []
}