{
  "abstract": "Nanoparticle self-assembly offers a powerful route to programmable functional materials, but its dynamics remain difficult to understand because they arise from multiscale, many-body, and environment-dependent interactions. Liquid-phase transmission electron microscopy (LPTEM) enables real-time observation of these dynamics, yet extracting the underlying physical laws from highly stochastic trajectories remains a significant challenge. Here we develop a physics-informed, time-varying autoregressive framework that learns multiscale self-assembly dynamics directly from LPTEM videos. Continuous Eulerian fields with local density and orientational order are coarse-grained from individual video frames and evolved by a sparse, nearest-neighbor operator optimized over a constrained matrix manifold. This interpretable approach captures long-time large scale phase transitions by tracking operator volatility over time, and short-timescale local evolution and transport rules. Crucially, we extract the underlying partial differential equations, where expected symmetries naturally emerge, enabling the direct inference of fundamental coefficients. Our framework provides a robust, computationally efficient tool for discovering hidden physical mechanisms from observational data, opening a path toward the predictive design of self-assembled nanostructures.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36498",
  "author_total": 7,
  "authors": [
    {
      "name": "Shuaifeng Li"
    },
    {
      "name": "Puquan Pan"
    },
    {
      "name": "Jonas Hallstrom"
    },
    {
      "name": "Nicholas Kotov"
    },
    {
      "name": "Paul Bogdan"
    },
    {
      "name": "Qian Chen"
    },
    {
      "name": "Xiaoming Mao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2srr3jgkd70r2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36498",
  "fulltext_url": null,
  "id": "2srr3jgkd70r2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36498",
  "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.mes-hall"
    ],
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7224133809",
  "pdf_url": "https://arxiv.org/pdf/2610.36498",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2srr3jgkd70r2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36498",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36498"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mes-hall",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Learning Multiscale Dynamics of Nanoparticle Self-Assembly",
  "title_text": "Learning Multiscale Dynamics of Nanoparticle Self-Assembly",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/2srr3jgkd70r2",
  "venue": "arXiv",
  "versions": []
}