Milica Todorovic
milica.todorovic@utu.fi +358 29 450 3619 +358 50 535 9519 Office: 302 My phone number is +358 50 331 0029. ORCID identifier: https://orcid.org/0000-0003-0028-0105 |
computational materials science; first principles simulations; hybrid organic/inorganic materials; surface science; artificial intelligence; data science; Bayesian optimization
My research focuses of first-principles simulations of advanced materials, with the objective to optimize their functional properties and performance in devices. The latest algorithms from computer science and artificial intelligence allow me to pursue data-smart approaches to materials design. In addition to materials science, physics and chemistry research, I have implemented data-driven solutions across disciplines, from aerosol research to chemical engineering bio-based materials, from computation to experiment.
I teach computational science courses for the MSc programme in Materials Engineering. I also teach the interdisciplinary MSc course "Machine Learning for Materials Science".
- Compositional engineering of perovskites with machine learning (2022)
- Physical Review Materials
(Refereed journal article or data article (A1)) - Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111) (2022)
- Physical Review B
(Refereed journal article or data article (A1)) - Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models (2022)
- MRS Bulletin
(Refereed journal article or data article (A1)) - Machine Learning Optimization of Lignin Properties in Green Biorefineries (2022)
- ACS Sustainable Chemistry and Engineering
(Refereed journal article or data article (A1)) - Machine learning sparse tight-binding parameters for defects (2022)
- npj Computational Materials
(Refereed journal article or data article (A1)) - Molecular Conformer Search with Low-Energy Latent Space (2022)
- Journal of Chemical Theory and Computation
(Refereed journal article or data article (A1)) - Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study (2022)
- ACS Applied Materials and Interfaces
(Refereed journal article or data article (A1)) - Roadmap on Machine learning in electronic structure (2022)
- Electronic Structure
(Refereed journal article or data article (A1)) - Efficient Amino Acid Conformer Search with Bayesian Optimization (2021)
- Journal of Chemical Theory and Computation
(Refereed journal article or data article (A1)) - Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization (2021)
- Machine Learning: Science and Technology
(Refereed journal article or data article (A1)) - Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations (2021)
- Advanced Functional Materials
(Refereed journal article or data article (A1)) - Predicting gas-particle partitioning coefficients of atmospheric molecules with machine learning (2021)
- Atmospheric Chemistry and Physics
(Refereed journal article or data article (A1)) - Atomic structures and orbital energies of 61,489 crystal-forming organic molecules (2020)
- Scientific Data
(Refereed journal article or data article (A1)) - Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search (2020)
- Advanced Science
(Refereed journal article or data article (A1)) - Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization (2020)
- Beilstein Journal of Nanotechnology
(Refereed journal article or data article (A1)) - Bayesian inference of atomistic structure in functional materials (2019)
- npj Computational Materials
(Refereed journal article or data article (A1)) - Chemical diversity in molecular orbital energy predictions with kernel ridge regression (2019)
- Journal of Chemical Physics
(Refereed journal article or data article (A1)) - Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (2019)
- Advanced Science
(Refereed journal article or data article (A1))