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
Fast and accurate predictions of covalent bonds in chemical space
K. Y. Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan +1
We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigate…
Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld +1
Elpasolite is the predominant quaternary crystal structure (AlNaKF prototype) reported in the Inorganic Crystal Structure Database. We have developed a machine learning mod…
Electronic spectra of linear HCH and cumulene carbene HC
M. Steglich, J. Fulara, S. Maity +2
The transition of linear HCH (A) has been observed in a neon matrix and gas phase. The assignment is based on mass-selective experiments, extrap…
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomi…
Electronic Spectra from TDDFT and Machine Learning in Chemical Space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza +1
Due to its favorable computational efficiency time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across ch…
Transferable atomic multipole machine learning models for small organic molecules
Tristan Bereau, Denis Andrienko, O. Anatole von Lilienfeld
Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular…
Crystal Structure Representations for Machine Learning Models of Formation Energies
Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld +1
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization e…
Big Data meets Quantum Chemistry Approximations: The -Machine Learning Approach
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp +1
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a com…
UV spectra of iron-doped carbon clusters FeC_n n = 3-6
Mathias Steglich, Xiaojing Chen, Anatoly Johnson +1
Electronic transitions of jet-cooled FeC clusters () were measured between 230 and 300 nm by a mass-resolved 1+1 resonant two-photon ionization technique. Rotational…