341 citations · 563 across the 5 of their papers we have counts for
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Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for -machine learning
Simon León Krug, Danish Khan, O. Anatole von Lilienfeld
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance . To accou…
Ab initio machine learning in chemical compound space
Bing Huang, O. Anatole von Lilienfeld
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first p…
FCHL revisited: faster and more accurate quantum machine learning
Anders S. Christensen, Lars A. Bratholm, Felix A. Faber +1
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 ato…
Operators in Machine Learning: Response Properties in Chemical Space
Anders S. Christensen, Felix A. Faber, O. Anatole von Lilienfeld
The role of response operators is well established in quantum mechanics. We investigate their use for universal quantum machine learning models of response properties in molecules.…
Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke
A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.