A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
Abstract
We address the degree to which machine learning (ML) can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based machine learning (MOB-ML) method is applied to several test systems. Strikingly, for the second-order Møller-Plessett perturbation theory, coupled cluster with singles and doubles (CCSD), and CCSD with perturbative triples levels of theory, it is shown that the thermally accessible (350 K) potential energy surface for a single water molecule can be described to within 1 mhartree using a model that is trained from only a single reference calculation at a randomized geometry. To explore the breadth of chemical diversity that can be described, MOB-ML is also applied to a new dataset of thermalized (350 K) geometries of 7211 organic models with up to seven heavy atoms. In comparison with the previously reported Δ-ML method, MOB-ML is shown to reach chemical accuracy with threefold fewer training geometries. Finally, a transferability test in which models trained for seven-heavy-atom systems are used to predict energies for thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with 36-fold fewer training calculations than Δ-ML (140 vs 5000 training calculations).
Additional Information
© 2019 Published under license by AIP Publishing. Submitted: 10 January 2019; Accepted: 19 March 2019; Published Online: 4 April 2019. We thank Daniel Smith (Molecular Sciences Software Institute) and Alberto Gobbi (Genentech) for a helpful discussion about available training datasets. T.F.M. acknowledges support from AFOSR Award No. FA9550-17-1-0102. A.S.C. acknowledges support from the National Centre of Competence in Research (NCCR) Materials Revolution: Computational Design and Discovery of Novel Materials (MARVEL) of the Swiss National Science Foundation (SNSF). We also acknowledge support from the Resnick Sustainability Institute postdoctoral fellowship (M.W.) and the Camille Dreyfus Teacher-Scholar Award (T.F.M.). Computational resources were provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the DOE Office of Science under Contract No. DE-AC02-05CH11231.Attached Files
Published - 1.5088393.pdf
Submitted - 1901.03309.pdf
Supplemental Material - supportinginformation.pdf
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Additional details
- Eprint ID
- 94699
- Resolver ID
- CaltechAUTHORS:20190415-085751643
- Air Force Office of Scientific Research (AFOSR)
- FA9550-17-1-0102
- Swiss National Science Foundation (SNSF)
- Resnick Sustainability Institute
- Camille and Henry Dreyfus Foundation
- Department of Energy (DOE)
- DE-AC02-05CH11231
- Created
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2019-04-16Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Resnick Sustainability Institute