skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

Abstract

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. As a result, our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [2]
+ Show Author Affiliations
  1. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Louisville, Louisville, KY (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1578167
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 9; Journal Issue: 3; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, and Curtiss, Larry. Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations. United States: N. p., 2019. Web. doi:10.1557/mrc.2019.107.
Copy to clipboard
Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, & Curtiss, Larry. Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations. United States. doi:10.1557/mrc.2019.107.
Copy to clipboard
Ward, Logan, Blaiszik, Ben, Foster, Ian, Assary, Rajeev S., Narayanan, Badri, and Curtiss, Larry. Tue . "Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations". United States. doi:10.1557/mrc.2019.107. https://www.osti.gov/servlets/purl/1578167.
Copy to clipboard
@article{osti_1578167,
title = {Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations},
author = {Ward, Logan and Blaiszik, Ben and Foster, Ian and Assary, Rajeev S. and Narayanan, Badri and Curtiss, Larry},
abstractNote = {Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. As a result, our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.},
doi = {10.1557/mrc.2019.107},
journal = {MRS Communications},
number = 3,
volume = 9,
place = {United States},
year = {2019},
month = {8}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI:  10.1557/mrc.2019.107
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited
journal, December 2018

  • Zaspel, Peter; Huang, Bing; Harbrecht, Helmut
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 3
  • DOI: 10.1021/acs.jctc.8b00832

The Materials Data Facility: Data Services to Advance Materials Science Research
journal, July 2016

Molecular graph convolutions: moving beyond fingerprints
journal, August 2016

  • Kearnes, Steven; McCloskey, Kevin; Berndl, Marc
  • Journal of Computer-Aided Molecular Design, Vol. 30, Issue 8
  • DOI: 10.1007/s10822-016-9938-8

MoleculeNet: a benchmark for molecular machine learning
journal, January 2018

  • Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.
  • Chemical Science, Vol. 9, Issue 2
  • DOI: 10.1039/C7SC02664A

A new mixing of Hartree–Fock and local density‐functional theories
journal, January 1993

  • Becke, Axel D.
  • The Journal of Chemical Physics, Vol. 98, Issue 2
  • DOI: 10.1063/1.464304

XSEDE: Accelerating Scientific Discovery
journal, September 2014

  • Towns, John; Cockerill, Timothy; Dahan, Maytal
  • Computing in Science & Engineering, Vol. 16, Issue 5
  • DOI: 10.1109/MCSE.2014.80

SchNet – A deep learning architecture for molecules and materials
journal, June 2018

  • Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J.
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5019779

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
journal, January 2018

  • Yao, Kun; Herr, John E.; Toth, David W.
  • Chemical Science, Vol. 9, Issue 8
  • DOI: 10.1039/C7SC04934J

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015

  • Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 5
  • DOI: 10.1021/acs.jctc.5b00099

Gaussian-4 theory using reduced order perturbation theory
journal, September 2007

  • Curtiss, Larry A.; Redfern, Paul C.; Raghavachari, Krishnan
  • The Journal of Chemical Physics, Vol. 127, Issue 12
  • DOI: 10.1063/1.2770701

Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
journal, April 2017

Quantum chemistry structures and properties of 134 kilo molecules
journal, August 2014

  • Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
  • Scientific Data, Vol. 1, Issue 1
  • DOI: 10.1038/sdata.2014.22

G n theory : G
journal, June 2011

  • Curtiss, Larry A.; Redfern, Paul C.; Raghavachari, Krishnan
  • Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 1, Issue 5
  • DOI: 10.1002/wcms.59

Next generation interatomic potentials for condensed systems
journal, July 2014

Multi-fidelity machine learning models for accurate bandgap predictions of solids
journal, March 2017

Open Babel: An open chemical toolbox
journal, October 2011

  • O'Boyle, Noel M.; Banck, Michael; James, Craig A.
  • Journal of Cheminformatics, Vol. 3, Issue 1
  • DOI: 10.1186/1758-2946-3-33

Atomistic calculations and materials informatics: A review
journal, June 2017

Machine learning for quantum mechanics in a nutshell
journal, July 2015

  • Rupp, Matthias
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24954

Alchemical and structural distribution based representation for universal quantum machine learning
journal, June 2018

  • Faber, Felix A.; Christensen, Anders S.; Huang, Bing
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5020710

Accurate quantum chemical energies for 133 000 organic molecules
journal, January 2019

  • Narayanan, Badri; Redfern, Paul C.; Assary, Rajeev S.
  • Chemical Science, Vol. 10, Issue 31
  • DOI: 10.1039/C9SC02834J

SchNetPack: A Deep Learning Toolbox For Atomistic Systems
journal, November 2018

  • Schütt, K. T.; Kessel, P.; Gastegger, M.
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 1
  • DOI: 10.1021/acs.jctc.8b00908

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
journal, March 2017

  • Browning, Nicholas J.; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole
  • The Journal of Physical Chemistry Letters, Vol. 8, Issue 7
  • DOI: 10.1021/acs.jpclett.7b00038

Predicting molecular properties with covariant compositional networks
journal, June 2018

  • Hy, Truong Son; Trivedi, Shubhendu; Pan, Horace
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5024797

A graph-convolutional neural network model for the prediction of chemical reactivity
journal, January 2019

  • Coley, Connor W.; Jin, Wengong; Rogers, Luke
  • Chemical Science, Vol. 10, Issue 2
  • DOI: 10.1039/C8SC04228D
    [ × clear filter / sort ]

    Works referencing / citing this record:

    A data ecosystem to support machine learning in materials science
    journal, October 2019

    • Blaiszik, Ben; Ward, Logan; Schwarting, Marcus
    • MRS Communications, Vol. 9, Issue 4
    • DOI: 10.1557/mrc.2019.118
      [ × clear filter / sort ]

      Similar Records in DOE PAGES and OSTI.GOV collections: