6.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR336 papers
- ETH ZurichCH215 papers
- Petersburg Nuclear Physics InstituteRU204 papers
- Massachusetts Institute of TechnologyUS182 papers
- European Organization for Nuclear ResearchCH168 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR167 papers
- University of GenevaCH163 papers
- Hungarian Academy of SciencesHU159 papers
- California Institute of TechnologyUS145 papers
- Paul Scherrer InstituteCH143 papers
- University of BolognaIT143 papers
- University of MichiganUS143 papers
9 papers · 2 filters
Alchemical normal modes unify chemical space
Stijn Fias, K. Y. Samuel Chang, O. Anatole von Lilienfeld
In silico design of new molecules and materials with desirable quantum properties by high-throughput screening is a major challenge due to the high dimensionality of chemical space…
Alchemical perturbation density functional theory (APDFT)
Guido Falk von Rudorff, O. Anatole von Lilienfeld
We introduce an orbital free electron density functional approximation based on alchemical perturbation theory. Given convergent perturbations of a suitable reference system, the a…
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.…
Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited
Peter Zaspel, Bing Huang, Helmut Harbrecht +1
Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approx…
The fundamentals of quantum machine learning
Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld
Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving a…
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.
AlGaAs crystals with direct 2 eV band gaps from computational alchemy
K. Y. Samuel Chang, O. Anatole von Lilienfeld
We use alchemical first order derivatives for the rapid yet robust prediction of band structures. The power of the approach is demonstrated for the design challenge of finding Al$_…
Observation of different reactivities of para- and ortho-water towards cold diazenylium ions
Ardita Kilaj, Hong Gao, Daniel Rösch +3
Water, HO, is one of the fundamental molecules in chemistry, biology and astrophysics. It exists as two distinct nuclear-spin isomers, para- and ortho-water, which do not inter…
Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for popular density functional approximations
D. Tahchieva, D. Bakowies, R. Ramakrishnan +1
The reliability of popular density functionals was studied for the description of torsional profiles of 36 molecules: glyoxal, oxalyl halides and their thiocarbonyl derivatives. HF…