3 papers
physics.chem-ph2020
Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization
Annika Stuke, Patrick Rinke, Milica Todorović
Machine learning methods usually depend on internal parameters -- so called hyperparameters -- that need to be optimized for best performance. Such optimization poses a burden on m…
physics.comp-ph2020
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules
Annika Stuke, Christian Kunkel, Dorothea Golze +5
Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets w…
physics.chem-ph2018
Chemical diversity in molecular orbital energy predictions with kernel ridge regression
Annika Stuke, Milica Todorović, Matthias Rupp +4
Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmar…