{
  "abstract": "SALTED provides an open-source Python package for machine learning the quantum-mechanical electron density, n(r), in molecular and condensed-phase systems based on input atomic coordinates and species. The program adopts a linear atom-centered decomposition of the electron density, which makes it highly transferable across diverse atomistic configurations sharing similar chemical environments. Because of this representation choice, SALTED is naturally interfaced with state-of-the-art electronic-structure programs based on atomic orbitals, namely CP2K, FHI-aims, and PySCF, from which reference electron-density data can be generated and used to train a model. The learning algorithm is based on a symmetry-adapted extension of Gaussian process regression, making SALTED especially efficient in small-data regimes. Thanks to the implementation of vector-field kernel functions, SALTED can also learn the first-order response of the electron density to applied electric fields, ∂n(r)/∂E. The application of SALTED within computational workflows has already shown its utility in a wide variety of contexts, including the calculation of polarization vectors and polarizability tensors, the accurate evaluation of Coulomb forces in QM/MM molecular-dynamics simulations, and electronic-structure studies of large-scale 2D materials.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03576",
  "author_total": 8,
  "authors": [
    {
      "name": "Zekun Lou"
    },
    {
      "name": "Alan M Lewis"
    },
    {
      "name": "Théophane Bernhard"
    },
    {
      "name": "Lukas Seifert"
    },
    {
      "name": "Agustin Salcedo"
    },
    {
      "name": "Florian Kleemiss"
    },
    {
      "name": "Mariana Rossi"
    },
    {
      "name": "Andrea Grisafi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a3wjxprf76p52.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03576",
  "fulltext_url": null,
  "id": "a3wjxprf76p52",
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  "landing_url": "https://hal.science/hal-05834959v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
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  "metadata_licence": "CC0-1.0",
  "native_subjects": [
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      "2505|Machine Learning in Materials Science"
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  "openalex": "W7208706040",
  "pdf_url": "https://hal.science/hal-05834959/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a3wjxprf76p52.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "hal-05834959",
      "source": "hal",
      "url": "https://hal.science/hal-05834959v1"
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    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.03576",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03576"
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "hal:0.chim",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials",
  "title_text": "SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/a3wjxprf76p52",
  "venue": "HAL",
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}