{
  "id": "1901.03309",
  "version": "v1",
  "published": "2019-01-10T18:26:45.000Z",
  "updated": "2019-01-10T18:26:45.000Z",
  "title": "A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules",
  "authors": [
    "Lixue Cheng",
    "Matthew Welborn",
    "Thomas F. Miller III"
  ],
  "comment": "7 pages, 3 figures",
  "categories": [
    "physics.chem-ph"
  ],
  "abstract": "We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. After presenting refined strategies for feature design and selection, the molecular-orbital-based machine learning (MOB-ML) method is first applied to benchmark test systems. It is shown that the total electronic energy for a set of 1000 randomized geometries of water can be described to within 1 millihartree using a model that is trained at the level of MP2, CCSD, or CCSD(T) using only a single reference calculation at a randomized geometry. To explore the breadth of chemical diversity that can be described, the MOB-ML method is then applied a set of 7211 organic models with up to seven heavy atoms. It is shown that MP2 calculations on only 90 molecules are needed to train a model that predicts MP2 energies to within 2 millihartree accuracy for the remaining 7121 molecules; likewise, CCSD(T) calculations on only 150 molecules are needed to train a model that predicts CCSD(T) energies for the remaining molecules to within 2 millihartree accuracy. The MP2 model, trained with only 90 reference calculations on seven-heavy-atom molecules, is then applied to a diverse set of 1000 thirteen-heavy-atom organic molecules, demonstrating transferable preservation of chemical accuracy.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-01-10T18:26:45.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "universal density matrix functional",
      "molecular orbital-based machine learning",
      "organic molecules",
      "transferably predict post-hartree-fock correlation energies",
      "transferability"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 7,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}