Andrea Anelli
Cited by
Cited by
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
Learning the electronic density of states in condensed matter
C Ben Mahmoud, A Anelli, G Csányi, M Ceriotti
Physical Review B 102 (23), 235130, 2020
Mapping uncharted territory in ice from zeolite networks to ice structures
EA Engel, A Anelli, M Ceriotti, CJ Pickard, RJ Needs
Nature communications 9 (1), 2173, 2018
A Bayesian approach to NMR crystal structure determination
EA Engel, A Anelli, A Hofstetter, F Paruzzo, L Emsley, M Ceriotti
Physical Chemistry Chemical Physics 21 (42), 23385-23400, 2019
Generalized Convex Hull Construction for Materials Discovery
A Anelli, EA Engel, CJ Pickard, M Ceriotti
Physical Review Materials 2, 103804, 2018
Exploring the robust extrapolation of high-dimensional machine learning potentials
C Zeni, A Anelli, A Glielmo, K Rossi
Physical Review B 105 (16), 165141, 2022
Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems
C Zeni, A Anelli, A Glielmo, S de Gironcoli, K Rossi
Digital Discovery 3 (1), 113-121, 2024
Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute–Lipid Interactions
JJ Lange, A Anelli, J Alsenz, M Kuentz, PJ O’Dwyer, W Saal, ...
Molecular Pharmaceutics, 2024
Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry
K Atz, DF Nippa, AT Müller, V Jost, A Anelli, M Reutlinger, C Kramer, ...
RSC Medicinal Chemistry, 2024
Machine learning potentials always extrapolate, it does not matter.
C Zeni, A Anelli, A Glielmo, K Rossi
arXiv e-prints, arXiv: 2112.10434, 2021
Characterising Structure and Stability of Materials using Machine Learning
A Anelli
EPFL, 2020
Group ID U12743
A Anelli, E Baldi, B Mahmoud, F Chiheb Bigi, M Ceriotti, R Cersonsky, ...
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