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CORDEIRO FONSECA Gregory

University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Physics and Materials Science > Team Alexandre TKATCHENKO

Main Referenced Co-authors
POLTAVSKYI, Igor  (2)
TKATCHENKO, Alexandre  (2)
VASSILEV GALINDO, Valentin  (2)
Main Referenced Keywords
machine learning, force fields, molecular dynamics, machine learning force fields, interatomic potential, molecular simulations (1);
Main Referenced Disciplines
Physics (3)

Publications (total 3)

The most downloaded
199 downloads
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 https://hdl.handle.net/10993/49417

The most cited

32 citations (OpenAlex)

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 https://hdl.handle.net/10993/49417

CORDEIRO FONSECA, G. (2023). Machine Learning Force Fields under the microscope: stability, reliability and performance analysis [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/60823

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

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

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