{
  "abstract": "The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and produce non-convex latent representations that complicate continuous optimization for inverse design. These architectures are impractical in data-scarce regimes, which is the typical case in molecular screening, and exhibit well-documented limitations in capturing chemically disordered configurations and chiral geometries. This review presents feature engineering based on spatial statistics as a physically rigorous and immediately deployable alternative. Molecular structures are encoded as voxelized scalar fields, and two-point auto- and cross-correlations are evaluated deterministically via Fast Fourier Transforms, explicitly transferring the burden of spatial pattern recognition from the learning algorithm to a closed-form, physics-informed operation. Principal component analysis of the resulting correlation maps yields low-dimensional, strictly convex representations that support lean neural networks (<100k trainable parameters) and non-parametric surrogate models, achieving sub-2% prediction error with as few as 10 training samples. Demonstrated across periodic crystals, chemically disordered high-entropy alloys, and non-periodic organic molecules, this framework enables Bayesian active learning and zero-shot extrapolation on commodity hardware, which current large-scale architectures cannot replicate at equivalent data budgets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.17147",
  "author_total": 2,
  "authors": [
    {
      "name": "Pranoy Ray"
    },
    {
      "name": "Surya R. Kalidindi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fbbr9v62ync5q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.17147",
  "fulltext_url": null,
  "id": "fbbr9v62ync5q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.17147",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.mes-hall"
    ],
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.17147",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fbbr9v62ync5q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2605.17147",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.17147"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Spatial statistics for screening molecular structures",
  "title_text": "Spatial statistics for screening molecular structures",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:04Z",
  "url": "https://research.draconitic.com/paper/fbbr9v62ync5q",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1002/smll.76291",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}