{
  "abstract": "Melting temperature is a critical property for high-temperature materials design, but first-principles melting calculations based on finite-temperature molecular dynamics can require substantial computational resources. The SLUSCHI method reduces this cost by using small-cell solid--liquid coexistence simulations and statistical analysis of many short molecular-dynamics trajectories. Here I present SLUSCHI-UP, a deployed web service for atomistic melting-temperature estimation that couples the SLUSCHI workflow to selectable pretrained universal machine-learning interatomic potentials (uMLIPs) and asynchronous GPU execution. Users submit a crystal structure through a Materials Project identifier or POSCAR input, select a uMLIP backend, and launch a queued melting calculation without local installation of simulation software. The current production interface supports mace-mpa-0-medium, Allegro-OAM-L, and DPA-3.2-5M-OMat24, while beta deployments expose additional models. On the compact MeltBench-10 validation set, the three production backends produce raw coexistence mean absolute errors in the range of approximately 178--327 K. Across the broader set of materials tested so far in MeltBench, the current deployed-job snapshot contains 119 raw uMLIP entries, and PBE-corrected Allegro-OAM-L predictions reach a mean absolute error of approximately 166 K. These values should be interpreted as screening-level infrastructure validation rather than a definitive ranking of uMLIPs. The results demonstrate that SLUSCHI-UP provides a practical, provenance-aware deployment layer between fast scalar melting-temperature predictors and much more expensive first-principles coexistence calculations, while retaining the usual limitations of uMLIP transferability, finite-size sampling, and high-temperature trajectory stability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.04973",
  "author_total": 1,
  "authors": [
    {
      "name": "Qi-Jun Hong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0yzy800w7wef7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.04973",
  "fulltext_url": null,
  "id": "0yzy800w7wef7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.04973",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.04973",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0yzy800w7wef7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:48Z",
      "native_id": "2606.04973",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.04973"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials",
  "title_text": "SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/0yzy800w7wef7",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.commatsci.2026.115108",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}