M. Todorović

发表

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

M. Rupp, P. Rinke, A. Stuke, 2018, The Journal of chemical physics.

Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials

M. Gutmann, J. Corander, P. Rinke, 2017 .

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Mikkel N. Schmidt, Aki Vehtari, P. Rinke, 2019, Advanced science.

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

P. Rinke, M. Todorović, J. Järvi, 2020, Beilstein journal of nanotechnology.

Submolecular Imaging by Noncontact Atomic Force Microscopy with an Oxygen Atom Rigidly Connected to a Metallic Probe.

H. Fuchs, M. Todorović, R. Pérez, 2016, ACS nano.

Machine Learning as a Tool to Engineer Microstructures: Morphological Prediction of Tannin-Based Colloids Using Bayesian Surrogate Models

P. Rinke, M. Todorović, O. Rojas, 2021 .

Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models

P. Rinke, M. Todorović, O. Rojas, 2022, MRS bulletin.

Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models

P. Rinke, M. Todorović, O. Rojas, 2021, MRS Bulletin.

Machine Learning Optimization of Lignin Properties in Green Biorefineries

P. Rinke, M. Todorović, Dmitry E. Tarasov, 2022, ACS Sustainable Chemistry & Engineering.

Machine learning sparse tight-binding parameters for defects

P. Rinke, M. Todorović, Kunal Ghosh, 2022, npj Computational Materials.

Roadmap on Machine learning in electronic structure

Kristof T. Schütt, T. Carrington, S. Manzhos, 2022, Electronic Structure.

Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111)

P. Rinke, M. Todorović, J. Järvi, 2022, Physical Review B.

Simultaneous Measurement of Multiple Independent Atomic-Scale Interactions Using Scanning Probe Microscopy: Data Interpretation and the Effect of Cross-Talk

M. Todorović, E. Altman, U. Schwarz, 2015 .

Compositional engineering of perovskites with machine learning

P. Rinke, M. Todorović, Jingrui Li, 2022, Physical Review Materials.

Isopiestic Determination of the Osmotic and Activity Coefficients of K2HPO4(aq), Including Saturated and Supersaturated Solutions, at T=298.15 K

J. A. Rard, M. Todorović, M. Zrilić, 2011 .

Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search

Alexander T. Egger, P. Rinke, M. Todorović, 2020, Advanced Science.

Osmotic and Activity Coefficients of {yKCl+(1−y)MgCl2}(aq) at T=298.15 K

M. Todorović, J. Miladinović, R. Ninković, 2007 .

Isopiestic Investigation of the Osmotic and Activity Coefficients of {yMgCl2+(1−y)MgSO4}(aq) and the Osmotic Coefficients of Na2SO4⋅MgSO4(aq) at 298.15 K

J. A. Rard, M. Todorović, J. Miladinović, 2007 .

Osmotic and activity coefficients of {xK2SO4+(1 −x)MgSO4}(aq) at the temperature 298.15 K

M. Todorović, R. Ninković, 1995 .

Osmotic coefficient of K2SO4(aq) in supersaturated solution at T = 298.15 K

M. Todorović, J. Miladinović, B. Božović, 2005 .

Osmotic and Activity Coefficients of the {xZnCl2+(1−x)ZnSO4}(aq) System at 298.15 K

J. A. Rard, M. Todorović, J. Miladinović, 2006 .

Osmotic coefficient of the ZnSO4(aq) at T=298.15 K

M. Todorović, J. Miladinović, R. Ninković, 2002 .

Correlation of Osmotic Coefficient Data for ZnSO4(aq) at 25°C by Various Thermodynamic Models

M. Todorović, V. Jovanović, J. Miladinović, 2003 .

Pentacene/TiO2 Anatase Hybrid Interface Study by Scanning Probe Microscopy and First Principles Calculations.

M. Todorović, R. Pérez, O. Custance, 2018, ACS applied materials & interfaces.

Atomic species identification at the (101) anatase surface by simultaneous scanning tunnelling and atomic force microscopy

M. Todorović, D. Fujita, E. Palomares, 2015, Nature Communications.

Understanding scanning tunneling microscopy contrast mechanisms on metal oxides: a case study.

Milica Todorović, Rubén Pérez, M. Todorović, 2013, ACS nano.

Influence of Hydrolysis on the Osmotic Coefficients of the System {yZnCl2 + (1 -y)ZnSO4}(aq) at Temperature 298.15 K

M. Todorović, J. Miladinović, R. Ninković, 2009 .

Osmotic coefficient of the ZnCl2(aq) at T=298.15 K

M. Todorović, V. Jovanović, J. Miladinović, 2003 .

Bayesian inference of atomistic structure in functional materials

Jukka Corander, Patrick Rinke, Milica Todorović, 2017, npj Computational Materials.

Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning

P. Rinke, M. Todorović, H. Vehkamäki, 2020, Atmospheric Chemistry and Physics.

First Principles Calculations on the Stoichiometric and Defective (101) Anatase Surface and Upon Hydrogen and H2Pc Adsorption: The Influence of Electronic Exchange and Correlation and of Basis Set Approximations

M. Todorović, N. Harrison, R. Pérez, 2019, Front. Chem..

Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study

P. Rinke, M. Dvorak, M. Todorović, 2022, ACS applied materials & interfaces.

Projective Preferential Bayesian Optimization pdf

Samuel Kaski, Petrus Mikkola, Milica Todorovic, 2020, ICML.

Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization

P. Rinke, M. Todorović, Xiaomi Guo, 2023, J. Chem. Inf. Model..

Gold diggers: Altered reconstruction of the gold surface by physisorbed aromatic oligomers

P. Rinke, Dorothea Golze, M. Todorović, 2019, Physical Review Materials.

Pseudo-atomic orbitals as basis sets for the O(N) DFT code CONQUEST

David R. Bowler, Antonio S. Torralba, Tsuyoshi Miyazaki, 2008 .

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

P. Rinke, K. Reuter, H. Oberhofer, 2020, Scientific Data.

Density-functional theory study of gramicidin A ion channel geometry and electronic properties

M. Gillan, D. Bowler, T. Miyazaki, 2013, Journal of The Royal Society Interface.

Atom-specific forces and defect identification on surface-oxidized Cu(100) with combined 3D-AFM and STM measurements

M. Todorović, E. Altman, U. Schwarz, 2013 .

Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules

P. Rinke, M. Todorović, H. Vehkamäki, 2023, Scientific data.

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization

Patrick Rinke, Annika Stuke, Milica Todorovi'c, 2020, Mach. Learn. Sci. Technol..

Efficient Amino Acid Conformer Search with Bayesian Optimization

P. Rinke, M. Todorović, Xi Chen, 2021, Journal of chemical theory and computation.

Efficient Cysteine Conformer Search with Bayesian Optimization

Milica Todorovic, Patrick Rinke, P. Rinke, 2020, 2006.15006.

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

M. Rupp, P. Rinke, A. Stuke, 2018, The Journal of chemical physics.

Molecular Conformer Search with Low-Energy Latent Space

Yong Xu, W. Duan, P. Rinke, 2022, Journal of chemical theory and computation.

Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)

Mikkel N. Schmidt, P. Rinke, A. Stuke, 2019, Advanced Science.