Education and Research Experience: 

BS (2004-2008), National Technical University of Ukraine

M.Sc. (2008-2010), University of Erlangen-Nürnberg

Mag. (2008-2010), National Technical University of Ukraine

Dr.(2010-2013), University of Erlangen-Nürnberg

Post-doc (2013-2019), Max-Planck-Institut für Kohlenforschung

Associate Professor (from 2019), Xiamen University

Principal Research Interests:
Development of efficient machine learning-based methods for atomistic simulations using our package MLatom. Development of the most accurate and consistent NDDO-based semiempirical quantum chemical methods. Development of hybrid machine learning/quantum chemical approaches. Application of a wide range of quantum chemical methods to real-world physicochemical problems.

Selected Recent Publications:

1. 1. Bao-Xin Xue, Mario Barbatti*, Pavlo O. Dral*, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310.

2. 2. Miriam Hauschild, Michal Borkowski, Pavlo O. Dral, Tomasz Marszalek, Frank Hampel, Gaozhan Xie, Jan Freudenberg, Uwe H. F. Bunz*, Milan Kivala*, 5,7,12,14-Tetraphenyl-Substituted 6,13-Diazapentacenes as Versatile Organic Semiconductors: Characterization in Field Effect Transistors. Org. Mater. 2020, 3, 204–213. DOI: 10.1055/s-0040-1713856.

3. 3. Marcel Krug, Maximilian Wagner, Tobias A. Schaub, Wen-Shan Zhang, Christoph M. Schü?lbauer, Johannes D. R. Ascherl, Peter M. Münich, Rasmus R. Schr?der, Franziska Gr?hn, Pavlo O. Dral, Mario Barbatti, Dirk M. Guldi*, Milan Kivala*, The Impact of Aggregation on the Photophysics of Spiro-bridged Heterotriangulenes. Angew. Chem. Int. Ed. 2020, 59, 16233–16240. DOI: 10.1002/anie.202003504.

4. 4. Pavlo O. Dral*, Alec Owens, Alexey Dral, Gábor Csányi*, Hierarchical Machine Learning of Potential Energy Surfaces. J. Chem. Phys. 2020, 152, 204110. DOI: 10.1063/5.0006498.

5. 5. Pavlo O. Dral*, Quantum Chemistry in the Age of Machine Learning. J. Phys. Chem. Lett. 2020, 11, 2336–2347. DOI: 10.1021/acs.jpclett.9b03664.

6. 6. Tobias A. Schaub, Theresa Mekelburg, Pavlo O. Dral, Matthias Miehlich, Frank Hampel, Karsten Meyer, Milan Kivala*, A Spherically Shielded Triphenylamine and Its Persistent Radical Cation. Chem. Eur. J. 2020, 26, 3264–3269. DOI: 10.1002/chem.202000355.

7. 7. Pavlo O. Dral*, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

8. 8. Pavlo O. Dral*, Xin Wu, Walter Thiel*, Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections. J. Chem. Theory Comput. 2019, 15, 1743–1760. DOI: 10.1021/acs.jctc.8b01265.

9. 9. Pavlo O. Dral*, Mario Barbatti*, Walter Thiel*, Nonadiabatic Excited-State Dynamics with Machine Learning. J. Phys. Chem. Lett. 2018, 9, 5660–5663. DOI: 10.1021/acs.jpclett.8b02469.

10. 10. Pavlo O. Dral*, Alec Owens, Sergei N. Yurchenko, Walter Thiel, Structure-Based Sampling and Self-Correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels. J. Chem. Phys. 2017, 146, 244108. DOI: 10.1063/1.4989536.

11. 11. Pavlo O. Dral*, Timothy Clark*, On the Feasibility of Reactions through the Fullerene Wall: A Theoretical Study of NHx@C60. Phys. Chem. Chem. Phys. 2017, 19, 17199–17209. DOI: 10.1039/C7CP02865B.

12. 12. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld*, Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. J. Chem. Theory Comput. 2015, 11, 2087–2096. DOI: 10.1021/acs.jctc.5b00099.

13. 13. Pavlo O. Dral*, O. Anatole von Lilienfeld, Walter Thiel*, Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations. J. Chem. Theory Comput.2015, 11, 2120–2125. DOI: 10.1021/acs.jctc.5b00141.

14. 14. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld*, Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Sci. Data 2014, 1, 140022. DOI: 10.1038/sdata.2014.22.

15. 15. Pavlo O. Dral*, The Unrestricted Local Properties: Application in Nanoelectronics and for Predicting Radicals Reactivity. J. Mol. Model. 2014, 20, 2134. DOI: 10.1007/s00894-014-2134-78.

Book chapter:
Pavlo O. Dral, Quantum Chemistry Assisted by Machine Learning. In Advances in Quantum Chemistry: Chemical Physics and Quantum Chemistry, Volume 81, 1st ed.; Kenneth Ruud, Erkki J. Br?ndas, Eds. Academic Press: 2020; Vol. 81. DOI: 10.1016/bs.aiq.2020.05.002.