Aldo Glielmo
Aldo Glielmo
Applied Research Team, Bank of Italy
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Accurate interatomic force fields via machine learning with covariant kernels
A Glielmo, P Sollich, A De Vita
Physical Review B 95 (21), 214302, 2017
Efficient nonparametric n-body force fields from machine learning
A Glielmo, C Zeni, A De Vita
Physical Review B 97 (18), 184307, 2018
Unsupervised learning methods for molecular simulation data
A Glielmo, BE Husic, A Rodriguez, C Clementi, F Noé, A Laio
Chemical Reviews 121 (16), 9722-9758, 2021
Building machine learning force fields for nanoclusters
C Zeni, K Rossi, A Glielmo, Á Fekete, N Gaston, F Baletto, A De Vita
The Journal of chemical physics 148 (24), 241739, 2018
SPONGE: A generalized eigenproblem for clustering signed networks
M Cucuringu, P Davies, A Glielmo, H Tyagi
Proceedings of Machine Learning Research 89, 1088-1098, 2019
On machine learning force fields for metallic nanoparticles
C Zeni, K Rossi, A Glielmo, F Baletto
Advances in Physics: X 4 (1), 1654919, 2019
Machine Learning Meets Quantum Physics
A Glielmo, C Zeni, Á Fekete, A De Vita
Schütt, KT, Chmiela, S., von Lilienfeld, OA, Tkatchenko, A., Tsuda, K …, 2020
Can we obtain the coefficient of restitution from the sound of a bouncing ball?
M Heckel, A Glielmo, N Gunkelmann, T Pöschel
Physical Review E 93 (3), 032901, 2016
Coefficient of restitution of aspherical particles
A Glielmo, N Gunkelmann, T Pöschel
Physical Review E 90 (5), 052204, 2014
Compact atomic descriptors enable accurate predictions via linear models
C Zeni, K Rossi, A Glielmo, S De Gironcoli
The Journal of Chemical Physics 154 (22), 224112, 2021
Gaussian Process States: A data-driven representation of quantum many-body physics
A Glielmo, Y Rath, G Csanyi, A De Vita, GH Booth
Physical Review X 10 (4), 041026, 2020
Ranking the information content of distance measures
A Glielmo, C Zeni, B Cheng, G Csányi, A Laio
PNAS Nexus 1 (2), pgac039, 2022
Hierarchical nucleation in deep neural networks
D Doimo, A Glielmo, A Laio, A Ansuini
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
Building Nonparametric n-Body Force Fields Using Gaussian Process Regression
A Glielmo, C Zeni, A Fekete, A De Vita
Machine Learning Meets Quantum Physics, 67-98, 2020
Enabling QM-accurate simulation of dislocation motion in and using a hybrid multiscale approach
F Bianchini, A Glielmo, JR Kermode, A De Vita
Physical Review Materials 3 (4), 043605, 2019
A Bayesian inference framework for compression and prediction of quantum states
Y Rath, A Glielmo, GH Booth
The Journal of chemical physics 153 (12), 124108, 2020
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
DADApy: Distance-based analysis of data-manifolds in Python
A Glielmo, I Macocco, D Doimo, M Carli, C Zeni, R Wild, M d’Errico, ...
Patterns 3 (10), 100589, 2022
Stochastic nature of particle collisions and its impact on granular material properties
N Gunkelmann, D Serero, A Glielmo, M Montaine, M Heckel, T Pöschel
Particles in Contact: Micro Mechanics, Micro Process Dynamics and Particle …, 2019
Intrinsic dimension estimation for discrete metrics
I Macocco, A Glielmo, J Grilli, A Laio
arXiv preprint arXiv:2207.09688, 2022
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
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