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
5 papers · 2 filters
Alchemical and structural distribution based representation for improved QML
Felix A. Faber, Anders S. Christensen, Bing Huang +1
We introduce a representation of any atom in any chemical environment for the generation of efficient quantum machine learning (QML) models of common electronic ground-state proper…
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau, Robert A. DiStasio, Alexandre Tkatchenko +1
Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a co…
Quantum machine learning using atom-in-molecule-based fragments selected on-the-fly
Bing Huang, O. Anatole von Lilienfeld
First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computationa…
Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives
Yasmine S. Al-Hamdani, Angelos Michaelides, O. Anatole von Lilienfeld
The design and production of novel 2-dimensional materials has seen great progress in the last decade, prompting further exploration of the chemistry of such materials. Doping and…
Machine learning prediction errors better than DFT accuracy
Felix A. Faber, Luke Hutchison, Bing Huang +7
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state propert…