Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the…

• UD
  U.S. Department of Energy

  Awards: AC02-06CH11357, DE-AC02, 06CH11357, DE-AC02-06CH11357, DE-AC02-
• UB
  Universität Basel
• SN
  Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

  Award: PP00P2_138932
• OO
  Office of Science

  Awards: DE-AC02-06CH11357, DE-AC02, 06CH11357, AC02-06CH11357
• AN
  Argonne National Laboratory

  Awards: DE-AC02, 06CH11357, AC02-06CH11357