{
  "id": "1910.04132",
  "version": "v1",
  "published": "2019-10-09T17:17:39.000Z",
  "updated": "2019-10-09T17:17:39.000Z",
  "title": "Taming nuclear complexity with a committee of deep neural networks",
  "authors": [
    "D. Regnier",
    "R. -D. Lasseri",
    "J. -P. Ebran",
    "A. Penon"
  ],
  "categories": [
    "nucl-th",
    "physics.comp-ph"
  ],
  "abstract": "We demonstrate that a committee of deep neural networks is capable of predicting the ground-state and excited energies of more than 1800 atomic nuclei with an accuracy akin to the one achieved by state-of-the-art nuclear energy density functionals (EDFs) and a major speed-up. An active learning strategy is proposed to train this algorithm with a minimal set of 210 nuclei. This approach enables future fast studies of the influence of EDFs parametrizations on structure properties over the whole nuclear chart and suggests that for the first time an artificial intelligence successfully encoded the laws of nuclear deformation.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-10-09T17:17:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural networks",
      "taming nuclear complexity",
      "state-of-the-art nuclear energy density functionals"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}