922 citations · 1.6k across the 25 of their papers we have counts for
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Machine Learning, Quantum Mechanics, and Chemical Compound Space
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemi…
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 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…
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…
Many Molecular Properties from One Kernel in Chemical Space
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varyi…