English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

Dral, P. O., Owens, A., Yurchenko, S. N., & Thiel, W. (2017). Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels. The Journal of Chemical Physics, 146(24): 244108. doi:10.1063/1.4989536.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Dral, Pavlo O.1, Author              
Owens, Alec1, 2, Author              
Yurchenko, Sergei N.2, Author
Thiel, Walter1, Author              
Affiliations:
1Research Department Thiel, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445590              
2Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT London, United Kingdom, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). We introduce structure-based sampling to automatically assign nuclear configurations from a pre-defined grid to the training and prediction sets, respectively. Accurate high-level ab initio energies are required only for the points in the training set, while the energies for the remaining points are provided by the ML model with negligible computational cost. The proposed sampling procedure is shown to be superior to random sampling and also eliminates the need for training several ML models. Self-correcting machine learning has been implemented such that each additional layer corrects errors from the previous layer. The performance of our approach is demonstrated in a case study on a published high-level ab initio PES of methyl chloride with 44 819 points. The ML model is trained on sets of different sizes and then used to predict the energies for tens of thousands of nuclear configurations within seconds. The resulting datasets are utilized in variational calculations of the vibrational energy levels of CH3Cl. By using both structure-based sampling and self-correction, the size of the training set can be kept small (e.g., 10% of the points) without any significant loss of accuracy. In ab initio rovibrational spectroscopy, it is thus possible to reduce the number of computationally costly electronic structure calculations through structure-based sampling and self-correcting KRR-based machine learning by up to 90%.

Details

show
hide
Language(s): eng - English
 Dates: 2017-02-022017-06-092017-06-272017-06-28
 Publication Status: Published in print
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4989536
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: The Journal of Chemical Physics
  Abbreviation : J. Chem. Phys.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 146 (24) Sequence Number: 244108 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226