FCHL revisited: Faster and more accurate quantum machine learning

Anders S. Christensen, Lars A. Bratholm, Felix A. Faber, O. Anatole Von Lilienfeld*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

68 Citations (Scopus)
110 Downloads (Pure)


We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our previous work [F. A. Faber et al., J. Chem. Phys. 148, 241717 (2018)] where the representation is discretized and the individual features are rigorously optimized using Monte Carlo optimization. Combined with a Gaussian kernel function that incorporates elemental screening, chemical accuracy is reached for energy learning on the QM7b and QM9 datasets after training for minutes and hours, respectively. The model also shows good performance for non-bonded interactions in the condensed phase for a set of water clusters with a mean absolute error (MAE) binding energy error of less than 0.1 kcal/mol/molecule after training on 3200 samples. For force learning on the MD17 dataset, our optimized model similarly displays state-of-the-art accuracy with a regressor based on Gaussian process regression. When the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom. The model presented herein is fast and lightweight enough for use in general chemistry problems as well as molecular dynamics simulations.

Original languageEnglish
Article number044107 (2020)
Number of pages16
JournalJournal of Chemical Physics
Issue number4
Early online date27 Jan 2020
Publication statusPublished - 31 Jan 2020


Dive into the research topics of 'FCHL revisited: Faster and more accurate quantum machine learning'. Together they form a unique fingerprint.

Cite this