Follow
Giulio Imbalzano
Giulio Imbalzano
EPFL
Verified email at epfl.ch
Title
Cited by
Cited by
Year
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
G Imbalzano, A Anelli, D Giofré, S Klees, J Behler, M Ceriotti
The Journal of chemical physics 148 (24), 2018
3392018
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
TT Nguyen, E Székely, G Imbalzano, J Behler, G Csányi, M Ceriotti, ...
The Journal of chemical physics 148 (24), 2018
2072018
Uncertainty estimation for molecular dynamics and sampling
G Imbalzano, Y Zhuang, V Kapil, K Rossi, EA Engel, F Grasselli, M Ceriotti
The Journal of Chemical Physics 154 (7), 074102, 2021
832021
The role of feature space in atomistic learning
A Goscinski, G Fraux, G Imbalzano, M Ceriotti
Machine Learning: Science and Technology 2 (2), 025028, 2021
372021
3D ordering at the liquid–solid polar interface of nanowires
M Zamani, G Imbalzano, N Tappy, DTL Alexander, S Martí‐Sánchez, ...
Advanced Materials 32 (38), 2001030, 2020
182020
Modeling the Ga/As binary system across temperatures and compositions from first principles
G Imbalzano, M Ceriotti
Physical Review Materials 5 (6), 063804, 2021
172021
First principle calculations of the residual resistivity of defects in metals
G Imbalzano
52015
Transferable machine-learning models of complex materials: the case of GaAs
G Imbalzano
EPFL, 2021
2021
Group ID U12743
A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ...
The system can't perform the operation now. Try again later.
Articles 1–9