| Combining machine learning and computational chemistry for predictive insights into chemical systems JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ... Chemical Reviews 121 (16), 9816-9872, 2021 | 644 | 2021 |
| Planar pentacoordinate carbons V Vassilev-Galindo, S Pan, KJ Donald, G Merino Nature Reviews Chemistry 2 (2), 0114, 2018 | 139 | 2018 |
| Accurate global machine learning force fields for molecules with hundreds of atoms S Chmiela, V Vassilev-Galindo, OT Unke, A Kabylda, HE Sauceda, ... Science Advances 9 (2), eadf0873, 2023 | 125 | 2023 |
| Planar pentacoordinate carbon atoms embedded in a metallocene framework Z Cui, V Vassilev-Galindo, JL Cabellos, E Osorio, M Orozco, S Pan, ... Chemical Communications 53 (1), 138-141, 2017 | 74 | 2017 |
| Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature HE Sauceda, V Vassilev-Galindo, S Chmiela, KR Müller, A Tkatchenko Nature Communications 12 (1), 442, 2021 | 47 | 2021 |
| Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules V Vassilev-Galindo, G Fonseca, I Poltavsky, A Tkatchenko The Journal of Chemical Physics 154 (9), 2021 | 38 | 2021 |
| Efficient interatomic descriptors for accurate machine learning force fields of extended molecules A Kabylda, V Vassilev-Galindo, S Chmiela, I Poltavsky, A Tkatchenko nature communications 14 (1), 3562, 2023 | 29* | 2023 |
| Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning G Fonseca, I Poltavsky, V Vassilev-Galindo, A Tkatchenko The Journal of Chemical Physics 154 (12), 2021 | 29 | 2021 |
| Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors M Gallegos, V Vassilev-Galindo, I Poltavsky, Á Martín Pendás, ... Nature Communications 15 (1), 4345, 2024 | 15 | 2024 |
| Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys B Yang, V Vassilev-Galindo, J Llorca npj Computational Materials 10 (1), 26, 2024 | 14 | 2024 |
| Modeling molecular ensembles with gradient-domain machine learning force fields AM Maldonado, I Poltavsky, V Vassilev-Galindo, A Tkatchenko, JA Keith Digital Discovery 2 (3), 871-880, 2023 | 11 | 2023 |
| Analysis of the CO2 absorption through a series of amines within the integration of a computational chemistry and process simulation scheme V Vassilev-Galindo, MH Matus, MA Morales-Cabrera International Journal of Greenhouse Gas Control 50, 198-205, 2016 | 6 | 2016 |
| Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions C Martínez-Alonso, V Vassilev-Galindo, BM Comer, F Abild-Pedersen, ... Catalysis Science & Technology, 2024 | 1 | 2024 |
| Machine learning force fields: towards modelling flexible molecules V Vassilev Galindo Unilu-University of Luxembourg, Luxembourg, Luxembourg, 2022 | | 2022 |
| Flexible Molecules Need More Flexible Machine Learning Force Fields V Vassilev Galindo, G Cordeiro Fonseca, I Poltavskyi, A Tkatchenko APS March Meeting Abstracts 2021, C22. 005, 2021 | | 2021 |
| Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning G Cordeiro Fonseca, I Poltavskyi, V Vassilev Galindo, A Tkatchenko APS March Meeting Abstracts 2021, C22. 012, 2021 | | 2021 |
| Accurate and Efficient ML Force Fields for Hundreds of Atoms S Chmiela, V Vassilev Galindo, H Sauceda, KR Muller, A Tkatchenko APS March Meeting Abstracts 2021, B22. 005, 2021 | | 2021 |
| Toward optimal descriptors for accurate machine learning of flexible molecules V Vassilev Galindo, I Poltavskyi, A Tkatchenko Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Nuclear quantum delocalization enhances non-covalent intramolecular interactions: A machine learning and path integral molecular dynamics study H Sauceda, V Vassilev Galindo, S Chmiela, KR Müller, A Tkatchenko Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Constructing Accurate Machine Learning Force Fields for Flexible Molecules V Vassilev Galindo, I Poltavskyi, A Tkatchenko APS March Meeting Abstracts 2019, K21. 009, 2019 | | 2019 |
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSCZweryfikowany adres z uni.lu
