{
  "abstract": "We present RuNNer 2.0, the \"Ruhr University Neural Network energy representation\", a highly optimized software suite for training and evaluating high-dimensional neural network potentials (HDNNPs) of the second, third, and fourth generation. Long-range electrostatics and charge equilibration (QEq) for the description of non-local charge transfer in fourth-generation (4G) HDNNPs are accelerated by quasi-linear-scaling plane-wave methods, reducing QEq computational complexity from $\\mathcal{O}(N^3)$ to $\\mathcal{O}(N\\log^2 N)$ such that linear or quasi-linear scaling is achieved across all HDNNP generations. An optimized memory management strategy eliminates the training overhead traditionally associated with long-range interactions, allowing 4G-HDNNPs to be trained with the same efficiency as their local counterparts. Developed in modern Fortran (2003/2008 standards), combined with a hybrid MPI/OpenMP parallelization scheme, RuNNer 2.0 has been designed to run efficiently in any CPU environment, from cost-effective local workstations to massive HPC clusters. Its modular library architecture facilitates straightforward binding to external simulation software; native interfaces to LAMMPS and the Atomic Simulation Environment (ASE) provide full access to all its features, including built-in committee-based uncertainty quantification. The high efficiency and scalability of the RuNNer 2.0 ecosystem are demonstrated through detailed benchmarks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.17978",
  "author_total": 14,
  "authors": [
    {
      "name": "Alexander L. M. Knoll"
    },
    {
      "name": "Moritz R. Schäfer"
    },
    {
      "name": "K. Nikolas Lausch"
    },
    {
      "name": "Moritz Gubler"
    },
    {
      "name": "Henry Wang"
    },
    {
      "name": "Richard Springborn"
    },
    {
      "name": "Redouan El Haouari"
    },
    {
      "name": "Alea Miako Liebetrau"
    },
    {
      "name": "Jonas A. Finkler"
    },
    {
      "name": "Emir Kocer"
    },
    {
      "name": "Marco Eckhoff"
    },
    {
      "name": "Stefan Goedecker"
    },
    {
      "name": "Gunnar Schmitz"
    },
    {
      "name": "Jörg Behler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cj9d4pz30v99g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.17978",
  "fulltext_url": null,
  "id": "cj9d4pz30v99g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.17978",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7169859928",
  "pdf_url": "https://arxiv.org/pdf/2607.17978",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cj9d4pz30v99g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2607.17978",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.17978"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials",
  "title_text": "RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/cj9d4pz30v99g",
  "venue": "arXiv",
  "versions": []
}