{
  "abstract": "ReaxFF is a molecular dynamics method that can be considered a good approximation to quantum methods for investigating reactive molecular systems consisting of ten thousand to one hundred thousand atoms. While ReaxFF is usually a much faster alternative to quantum methods, the force field consists of nearly 100 parameters per element, which makes the force field development a high dimensional optimization problem. In addition to the high-dimensionality, non-convexity and non-continuity make it a hard problem to optimize. We use random optimization along with coordinate search strategies to optimize efficiently and sample new parameter points that yield good molecular properties close to predefined `reference values' obtained from quantum mechanical methods for the $\\mathrm{Mo-S}$ system. We also provide empirical error guaranties starting from any random sample of inputs. We discover new points for the $\\mathrm{Mo-S}$ system at adjusted error levels of $13{,}000$ as compared to Sengul et al. (2022) at $70{,}000$ levels under the same loss function, registering over $80\\%$ improvement. We also extend our algorithm to an out-of-sample system, $\\mathrm{W-S}$, with no training data to record over $70\\%$ improvement over Sengul et al. (2021).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.12401",
  "author_total": 5,
  "authors": [
    {
      "name": "Arun Ravichandran"
    },
    {
      "name": "Michael L. Stein"
    },
    {
      "name": "Mert Y. Sengul"
    },
    {
      "name": "Ying Hung"
    },
    {
      "name": "Tirthankar Dasgupta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d4mnea5pq2eqp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.12401",
  "fulltext_url": null,
  "id": "d4mnea5pq2eqp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.12401",
  "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",
      "stat.AP"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7212752418",
  "pdf_url": "https://arxiv.org/pdf/2609.12401",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d4mnea5pq2eqp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "2609.12401",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.12401"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Optimization of ReaxFF parameters for the $\\mathrm{Mo-S}$ system using random optimization and coordinate search",
  "title_text": "Optimization of ReaxFF parameters for the Mo−S system using random optimization and coordinate search",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/d4mnea5pq2eqp",
  "venue": "arXiv",
  "versions": []
}