{
  "id": "2210.07882",
  "version": "v1",
  "published": "2022-10-14T15:04:10.000Z",
  "updated": "2022-10-14T15:04:10.000Z",
  "title": "Ensuring thermodynamic consistency with invertible coarse-graining",
  "authors": [
    "Shriram Chennakesavalu",
    "David J. Toomer",
    "Grant M. Rotskoff"
  ],
  "categories": [
    "cond-mat.stat-mech"
  ],
  "abstract": "Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared to atomistic models. Designing ``good'' coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We show that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-14T15:04:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "ensuring thermodynamic consistency",
      "coarse-grained model",
      "invertible coarse-graining",
      "sample fine-grained configurations",
      "core computational tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}