Journal article

Authors list: Ruth, M; Gerbig, D; Schreiner, PR

Publication year: 2022

Pages: 4846-4855

Journal: Journal of Chemical Theory and Computation

Volume number: 18

Issue number: 8

ISSN: 1549-9618

eISSN: 1549-9626

DOI Link: https://doi.org/10.1021/acs.jctc.2c00501

Publisher: American Chemical Society

Abstract: 
Accurate thermochemistry is essential in many chemical disciplines, such as astro-, atmospheric, or combustion chemistry. These areas often involve fleetingly existent intermediates whose thermochemistry is difficult to assess. Whenever direct calorimetric experiments are infeasible, accurate computational estimates of relative molecular energies are required. However, high-level computations, often using coupled cluster theory, are generally resource-intensive. To expedite the process using machine learning techniques, we generated a database of energies for small organic molecules at the CCSD(T)/cc-pVDZ, CCSD(T)/aug-cc-pVDZ, and CCSD(T)/cc-pVTZ levels of theory. Leveraging the power of deep learning by employing graph neural networks, we are able to predict the effect of perturbatively included triples (T), that is, the difference between CCSD and CCSD(T) energies, with a mean absolute error of 0.25, 0.25, and 0.28 kcal mol(-)(1) (R-2 of 0.998, 0.997, and 0.998) with the cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ basis sets, respectively. Our models were further validated by application to three validation sets taken from the S22 Database as well as to a selection of known theoretically challenging cases.

Harvard Citation style: Ruth, M., Gerbig, D. and Schreiner, P. (2022) Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning, Journal of Chemical Theory and Computation, 18(8), pp. 4846-4855. https://doi.org/10.1021/acs.jctc.2c00501

APA Citation style: Ruth, M., Gerbig, D., & Schreiner, P. (2022). Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning. Journal of Chemical Theory and Computation. 18(8), 4846-4855. https://doi.org/10.1021/acs.jctc.2c00501