635 citations · 775 across the 2 of their papers we have counts for
4 papers
Orbital-free Bond Breaking via Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +3
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic…
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre +5
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identifica…
Finding Density Functionals with Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +2
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on…
How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling +3
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the…