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Valentin Vassilev-Galindo
Valentin Vassilev-Galindo
IMDEA Materials Institute
Dirección de correo verificada de imdea.org - Página principal
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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
4052021
Planar pentacoordinate carbons
V Vassilev-Galindo, S Pan, KJ Donald, G Merino
Nature Reviews Chemistry 2 (2), 0114, 2018
1192018
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
662017
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
602023
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
362021
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
332021
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
222021
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
16*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
62016
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
42023
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
12024
Machine learning force fields: towards modelling flexible molecules
V Vassilev Galindo
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
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