Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited

Zaspel, Peter and Huang, Bing and Harbrecht, Helmut and von Lilienfeld, Anatole O.. (2019) Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited. Journal of Chemical Theory and Computation, 15 (3). pp. 1546-1559.

PDF - Published Version

Official URL: https://edoc.unibas.ch/69799/

Downloads: Statistics Overview


Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multilevel combination (C) technique, to combine various levels of approximations made when calculating molecular energies within quantum chemistry. When combined with quantum machine learning (QML) models, the resulting CQML model is a generalized unified recursive kernel ridge regression which exploits correlations implicitly encoded in training data comprised of multiple levels in multiple dimensions. Here, we have investigated up to three dimensions: Chemical space, basis set, and electron correlation treatment. Numerical results have been obtained for at- omization energies of a set of ∼7'000 organic molecules with up to 7 atoms (not counting hydrogens) containing CHONFClS, as well as for ∼6'000 constitutional isomers of C 7 H 10 O 2 . CQML learning curves for atomization energies suggest a dramatic reduction in necessary training samples calculated with the most accurate and costly method. In order to generate millisecond estimates of CCSD(T)/cc-pvdz atomization energies with prediction errors reaching chemical accuracy (∼1 kcal/mol), the CQML model requires only ∼100 training instances at CCSD(T)/cc-pvdz level, rather than thousands within conventional QML, while more training molecules are required at lower levels. Our results suggest a possibly favorable trade-off between various hierarchical approximations whose computational cost scales differently with electron number.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Former Organization Units Chemistry > Physikalische Chemie (Lilienfeld)
05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Computational Mathematics (Harbrecht)
UniBasel Contributors:Harbrecht, Helmut and Zaspel, Peter and Huang, Bing and von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
edoc DOI:
Last Modified:28 Nov 2022 17:06
Deposited On:20 Mar 2019 08:57

Repository Staff Only: item control page