{
  "abstract": "Rare events govern many important molecular processes but remain difficult to characterize within accessible simulation timescales. This challenge has motivated machine-learning methods to construct low-dimensional collective variables for specific objectives, including state discrimination, slow-mode identification, and committor-function approximation. Here we introduce SelfTICA, a self-supervised framework that uses contrastive learning on time-lagged configurations to learn a latent representation of the slow modes governing relevant transitions. Once learned, this representation is frozen and reused across downstream tasks, including collective variables construction, enhanced sampling, free-energy estimation, and committor learning. Compared with direct slow-mode optimization, SelfTICA improves training stability and provides collective variables even from limited and exploratory trajectories, accelerating rare-event sampling and enabling accurate free-energy estimation. The dynamical information encoded during pretraining also accelerates committor learning and enables characterization of transition-state regions. These results show that contrastive learning of slow dynamical representations provides a common foundation for rare-event sampling and mechanistic analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.15495",
  "author_total": 5,
  "authors": [
    {
      "name": "Kai Zhu"
    },
    {
      "name": "Jintu Zhang"
    },
    {
      "name": "Pietro Novelli"
    },
    {
      "name": "Tingjun Hou"
    },
    {
      "name": "Luigi Bonati"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/83m330qeexq9r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.15495",
  "fulltext_url": null,
  "id": "83m330qeexq9r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.15495",
  "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",
      "physics.chem-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7164945391",
  "pdf_url": "https://arxiv.org/pdf/2606.15495",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/83m330qeexq9r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2606.15495",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.15495"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "SelfTICA: contrastive learning of dynamical representations for rare-event sampling and characterization",
  "title_text": "SelfTICA: contrastive learning of dynamical representations for rare-event sampling and characterization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/83m330qeexq9r",
  "venue": "arXiv",
  "versions": []
}