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4 papers · 2 filters
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…