Milica Todorovic
milica.todorovic@utu.fi +358 29 450 3619 +358 50 535 9519 Työhuone: 302 My phone number is +358 50 331 0029. ORCID-tunniste: 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
I lead the Materials Informatics Laboratory group at UTU. My research combines aritifical intelligence algorithms and first-principles simulations with the objective to optimize functional materials and their performance in devices. At MIL, we purpuse data-driven solutions across disciplines, from aerosol research to chemical engineering bio-based materials, from computation to experiment.
Service:
Vice-director of Sustainable Materials and Manufacturing (SUSMAT) UTU profiling area
Vice-chair of COST Action CA22154 - Data-driven Applications towards the Engineering of functional Materials: an Open Network (DAEMON)
Vice-leader of Human-Centric Artificial Intelligence for Sustainable Future (HAIF), Doctoral Training Network
Co-lead of Finnish Centre for AI (FCAI) Highlight E: Data-driven design of materials
Head of the Modern Industrial Materials MSc track
MTEK0023 Data Visualisation and Analysis
MTEK0023 Simulations and New Materials
MTEK0024 Machine Learning for Materials Science
- Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations (2021)
- Advanced Functional Materials
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Predicting gas-particle partitioning coefficients of atmospheric molecules with machine learning (2021)
- Atmospheric Chemistry and Physics
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Atomic structures and orbital energies of 61,489 crystal-forming organic molecules (2020)
- Scientific Data
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search (2020)
- Advanced Science
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization (2020)
- Beilstein Journal of Nanotechnology
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Bayesian inference of atomistic structure in functional materials (2019)
- npj Computational Materials
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Chemical diversity in molecular orbital energy predictions with kernel ridge regression (2019)
- Journal of Chemical Physics
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (2019)
- Advanced Science
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )
