{
  "abstract": "Long-range interactions in machine-learning interatomic potentials are commonly treated by coupling learned sources through either a prescribed electrostatic kernel or a learned but configurationindependent one. Whether such a kernel suffices depends on the underlying screened interaction. We address this question by using Fourier neural operators (FNOs) to learn how latent source fields, built from the descriptors of a local backbone (here MACE), interact across the simulation cell. For a synthetic ionic system with tunable screening, a prescribed Coulomb kernel underestimates the longwavelength charge fluctuations by 43% under strong screening, whereas the FNO reproduces them within 3% and recovers 95–99% of the variance of the residual left by the local backbone. A linear FNO, which learns a configuration-independent kernel, performs equally well in the homogeneous case. Under inhomogeneous screening, rearranging the remote medium modulates the coupling between distant ions, a dependence that no configuration-independent kernel can represent. Among the models tested, only the nonlinear FNO responds to this modulation, and its force errors are 27–41% lower than those of configuration-independent models. In first-principles benchmarks of liquid water, lithium lanthanum zirconate, and Au 2 /MgO(001), MACE–FNO is comparable to a prescribed-kernel model for the bulk systems and more accurate for the interface. It further captures the reversal of the Au 2 wetting preference induced by remote Al dopants, with deviations below 5meV per cell from density functional theory. These results indicate that a configuration-dependent kernel becomes necessary once the screening medium varies, even though the energy remains quadratic in the charges for a fixed medium. Nonlinear FNOs, which learn such a kernel from energies and forces alone, offer a flexible route to environment-dependent long-range interactions.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Condensed Matter and Nanosciences (IMCN)",
    "UCLouvain",
    "WEL Research Institute"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wei Chen",
      "orcid": "0000-0002-7496-0341"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Gian-Marco Rignanese",
      "orcid": "0000-0002-1422-1205"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5202dw2eztk1m.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011048/v1",
  "fulltext_url": null,
  "id": "5202dw2eztk1m",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011048/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223678022",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011048/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5202dw2eztk1m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011048",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011048/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "When do machine-learning potentials need a configuration-dependent kernel? Learning long-range interactions with Fourier neural operators",
  "title_text": "When do machine-learning potentials need a configuration-dependent kernel? Learning long-range interactions with Fourier neural operators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/5202dw2eztk1m",
  "venue": "ChemRxiv",
  "versions": []
}