4 citations · 4 across the 1 of their papers we have counts for
6 papers
Predicting Gas-Particle Partitioning Coefficients of Atmospheric Molecules with Machine Learning
Emma Lumiaro, Milica Todorović, Theo Kurten +2
The formation, properties and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic…
Efficient Cysteine Conformer Search with Bayesian Optimization
Lincan Fang, Esko Makkonen, Milica Todorovic +2
Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determ…
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…
Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization
Jari Järvi, Patrick Rinke, Milica Todorović
Identifying the atomic structure of organic-inorganic interfaces is challenging with our current research tools. Interpreting the structure of complex molecular adsorbates from mic…
Projective Preferential Bayesian Optimization
Petrus Mikkola, Milica Todorović, Jari Järvi +2
Bayesian optimization is an effective method for finding extrema of a black-box function. We propose a new type of Bayesian optimization for learning user preferences in high-dimen…
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…