{
  "abstract": "The nudged elastic band (NEB) method is established, but a reproducible transition-state calculation requires more than the NEB equations. Path construction, calculator isolation, restart behaviour, convergence monitoring, provenance, failure handling, and the transfer from inexpensive potentials to first-principles checks must remain consistent throughout the workflow. We developed NEBwalk as an open-source, calculator-agnostic Python framework for NEB and climbing-image NEB (CI-NEB) calculations built around the Atomic Simulation Environment (ASE). NEBwalk v0.11.0 supports improved-tangent NEB forces, variable springs, FIRE optimisation, minimum-image-aware periodic geometry, linear, IDPP, and regularized geodesic-style interpolation, restartable runs, structured result export, fresh calculator construction for each image, MLIP-assisted image selection, DFT-labelled data export, an optional MACE fine-tuning and evaluation handoff, resumable campaign infrastructure, reproducibility bundles, and conservative Quantum ESPRESSO recovery logic. A September 2026 audit re-examined 20 historical system-calculator cases rather than treating old documentation as automatically valid: 13 were re-audited and reconfirmed from surviving output artefacts, while 7 are retained only as historical provenance and excluded from the current quantitative validation claim. Ten artefact-backed vacancy-migration cases also have DOI-traceable literature comparators for the same element, crystal structure, and migration-hop class; because the numerical settings differ, we use these only as physical-scale checks. In two matched-physical-input implementation tests, NEBwalk and ASE differ by no more than 0.04 meV in the barrier and about 0.002 Å in the converged path geometry. A MACEMP-0 H/Cu(111) calculation gives a converged fcc-to-hcp barrier of about 0.126 eV, close to published DFT values of about 0.13-0.15 eV, although the literature itself disagrees on the sign of the small fcc-hcp site splitting. NEBwalk is therefore not presented as a new NEB algorithm or as a replacement for ASE or first-principles engines. Its role is to keep path optimisation, MLIP screening, DFT validation, reproducibility, and failure-aware execution in one inspectable workflow.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical Engineering",
    "Dhaka University of Engineering & Technology (DUET",
    "Department of Chemical and Geological Sciences",
    "University of Modena and Reggio Emilia (UNIMORE",
    "Department of Mechanical Engineering"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Md. Rifat Khandaker",
      "orcid": "0000-0003-0520-4654"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Ali Ghiami-Shomami"
    },
    {
      "affs": [
        4,
        1
      ],
      "name": "Mohammad Asaduzzaman Chowdhury"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/483wdxky0ypfs.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010372/v1",
  "fulltext_url": null,
  "id": "483wdxky0ypfs",
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  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010372/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": "W7221655357",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010372/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/483wdxky0ypfs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010372",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010372/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "NEBwalk: A transparent and calculator-agnostic framework for reproducible nudged elastic band workflows with machine-learned potentials and firstprinciples calculators",
  "title_text": "NEBwalk: A transparent and calculator-agnostic framework for reproducible nudged elastic band workflows with machine-learned potentials and firstprinciples calculators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/483wdxky0ypfs",
  "venue": "ChemRxiv",
  "versions": []
}