135 citations · 291 across the 10 of their papers we have counts for
10 papers
Molecular conformer search with low-energy latent space
Xiaomi Guo, Lincan Fang, Yong Xu +4
Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as…
Protective Coating Interfaces for perovskite Solar Cell Materials: A first Principles Study
Azimatu Fangnon, Marc Dvorak, Ville Havu +3
The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the poten…
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…
Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials
Milica Todorović, Michael U. Gutmann, Jukka Corander +1
Tailoring the functional properties of advanced organic/inorganic heterogeonous devices to their intended technological applications requires knowledge and control of the microscop…
Density-functional theory study of gramicidin A ion channel geometry and electronic properties
Milica Todorović, D. R. Bowler, M. J. Gillan +1
Understanding the mechanisms underlying ion channel function from the atomic-scale requires accurate ab initio modelling as well as careful experiments. Here, we present a density…