{
  "abstract": "Constructing free-energy landscapes without prior knowledge of their structure often requires dense sampling of the configuration space, leading to prohibitive computational costs. Here, we introduce an efficient graph-based active-learning framework that adaptively constructs free-energy landscapes while preserving the connectivity required for free-energy determination. The method employs a Gaussian-process surrogate to identify informative configurations and connects each newly sampled set of points to the existing sampled region through a shortest-path search on a graph, enabling efficient, connected exploration required for accurate free-energy determination. We validate the framework using analytical two-and three-dimensional model energy landscapes as well as a many-body free-energy landscape derived from molecular dynamics simulations of polymer-grafted nanoparticles in a polymer melt. Across all cases, the method accurately recovers the multidimensional landscape at much lower effective sampling cost than dense or predetermined grid-based sampling, with sampling efficiency quantified by the cumulative length of the connected sampling tree constructed during active learning. More broadly, this work establishes a general framework for data-efficient exploration of high-dimensional free-energy landscapes in molecular and materials simulations.",
  "abstract_shown": true,
  "affiliations": [
    "Thomas Lord Department of Mechanical Engineering and Materials Science",
    "Duke University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Mohsen Farshad"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Gaurav Arya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dqsbdkys1j8ey.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009038/v1",
  "fulltext_url": null,
  "id": "dqsbdkys1j8ey",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009038/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": "W7213533343",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/dqsbdkys1j8ey.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15009038",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009038/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Efficient Free-Energy Landscape Construction Using Graph-Based Active Path Sampling",
  "title_text": "Efficient Free-Energy Landscape Construction Using Graph-Based Active Path Sampling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/dqsbdkys1j8ey",
  "venue": "ChemRxiv",
  "versions": []
}