Profil

POLTAVSKYI Igor

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)

Main Referenced Co-authors
TKATCHENKO, Alexandre  (11)
CORDEIRO FONSECA, Gregory  (2)
VASSILEV GALINDO, Valentin  (2)
Berndt, Richard (1)
Ceriotti, Michele (1)
Main Referenced Keywords
ab initio simulations (1); graphene (1); imaginary time path integrals (1); nuclear quantum effects (1); thermal protons transport (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Physics (10)
Mathematics (1)

Publications (total 11)

The most downloaded
1142 downloads
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Poltavskyi, I., Kristof T. Schütt, Tkatchenko, A., & Klaus-Robert Müller. (2021). Machine Learning Force Fields. Chemical Reviews. doi:10.1021/acs.chemrev.0c01111 https://hdl.handle.net/10993/49416

The most cited

725 citations (Scopus®)

Chmiela, S., Tkatchenko, A., Sauceda, H., Poltavskyi, I., Schuett, K., & Mueller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Science Advances, 3, 1603015. doi:10.1126/sciadv.1603015 https://hdl.handle.net/10993/31098

Tkatchenko, A., Kabylda, A., Poltavskyi, I., Vassilev-Galindo Valentin, & Chmiela Stefan. (2023). Efficient interatomic descriptors for accurate machine learning force fields of extended molecules. Nature Communications. doi:10.1038/s41467-023-39214-w
Peer reviewed

Poltavskyi, I., & Tkatchenko, A. (2021). Machine Learning Force Fields: Recent Advances and Remaining Challenges. Journal of Physical Chemistry Letters. doi:10.1021/acs.jpclett.1c01204
Peer Reviewed verified by ORBi

Cordeiro Fonseca, G., Poltavskyi, I., Vassilev Galindo, V., & Tkatchenko, A. (2021). Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning. Journal of Chemical Physics. doi:10.1063/5.0035530
Peer reviewed

Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Poltavskyi, I., Kristof T. Schütt, Tkatchenko, A., & Klaus-Robert Müller. (2021). Machine Learning Force Fields. Chemical Reviews. doi:10.1021/acs.chemrev.0c01111
Peer Reviewed verified by ORBi

Vassilev Galindo, V., Cordeiro Fonseca, G., Poltavskyi, I., & Tkatchenko, A. (2021). Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules. Journal of Chemical Physics. doi:10.1063/5.0038516
Peer reviewed

Poltavskyi, I., Kapil, V., Ceriotti, M., Kim, K. S., & Tkatchenko, A. (2020). Accurate description of nuclear quantum effects with high-order perturbed path integrals (HOPPI). Journal of Chemical Theory and Computation.
Peer Reviewed verified by ORBi

Jasper-Tönnies, T., Poltavskyi, I., Ulrich, S., Moje, T., Tkatchenko, A., Herges, R., & Berndt, R. (27 December 2018). Stability of functionalized platform molecules on Au(111). Journal of Chemical Physics, 149, 8. doi:10.1063/1.5059344
Peer Reviewed verified by ORBi

Poltavskyi, I., Tkatchenko, A., Mortazavi, M., & Zheng, L. (31 May 2018). Quantum tunneling of thermal protons through pristine graphene. Journal of Chemical Physics, 148 (20), 204707. doi:10.1063/1.5024317
Peer reviewed

Poltavskyi, I., DiStasio, R., & Tkatchenko, A. (14 March 2018). Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials. Journal of Chemical Physics, 148 (10), 102325. doi:10.1063/1.5006596
Peer Reviewed verified by ORBi

Chmiela, S., Tkatchenko, A., Sauceda, H., Poltavskyi, I., Schuett, K., & Mueller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Science Advances, 3, 1603015. doi:10.1126/sciadv.1603015
Peer reviewed

Maurer, R. J., Liu, W., Poltavskyi, I., Stecher, T., Oberhofer, H., Reuter, K., & Tkatchenko, A. (2016). Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111). Physical Review Letters, 116, 146101. doi:10.1103/PhysRevLett.116.146101
Peer Reviewed verified by ORBi

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