Efficient nonparametric A Glielmo, C Zeni, A De Vita
Physical Review B 97 (18), 184307, 2018
162 2018 Mattergen: a generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687, 2023
102 2023 Building machine learning force fields for nanoclusters C Zeni, K Rossi, A Glielmo, Á Fekete, N Gaston, F Baletto, A De Vita
Journal of Chemical Physics 148 (24), 9, 2018
59 2018 Data-driven simulation and characterisation of gold nanoparticle melting C Zeni, K Rossi, T Pavloudis, J Kioseoglou, S de Gironcoli, RE Palmer, ...
Nature Communications 12 (1), 6056, 2021
51 2021 On machine learning force fields for metallic nanoparticles C Zeni, K Rossi, A Glielmo, F Baletto
Advances in Physics: X 4 (1), 1654919, 2019
43 2019 Ranking the information content of distance measures A Glielmo, C Zeni, B Cheng, G Csányi, A Laio
PNAS nexus 1 (2), pgac039, 2022
40 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), 2022
32 2022 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
29 2022 Mattersim: A deep learning atomistic model across elements, temperatures and pressures H Yang, C Hu, Y Zhou, X Liu, Y Shi, J Li, G Li, Z Chen, S Chen, C Zeni, ...
arXiv preprint arXiv:2405.04967, 2024
25 2024 Compact atomic descriptors enable accurate predictions via linear models C Zeni, K Rossi, A Glielmo, S De Gironcoli
Journal of Chemical Physics 154 (22), 224112, 2021
22 2021 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
14 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
14 2020 Romina Wild, Maria d’Errico, Alex Rodriguez, and Alessandro Laio A Glielmo, I Macocco, D Doimo, M Carli, C Zeni
Dadapy: Distance-based analysis of datamanifolds in python. Patterns 3 (10 …, 2022
11 2022 MatterGen: a generative model for inorganic materials design, arXiv, 2024 C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687 10, 0
8 MatterGen: A generative model for inorganic materials design. 2023 C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
URL http://doi. org/10.48550/arXiv 2312, 2023
7 2023 Structural characterisation of nanoalloys for (photo) catalytic applications with the Sapphire library RM Jones, K Rossi, C Zeni, M Vanzan, I Vasiljevic, A Santana-Bonilla, ...
Faraday Discussions 242, 326-352, 2023
7 2023 MatterSim: A deep learning atomistic model across elements, temperatures and pressures (2024) H Yang, C Hu, Y Zhou, X Liu, Y Shi, J Li, G Li, Z Chen, S Chen, C Zeni, ...
arXiv preprint arXiv:2405.04967, 0
5 Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis K Rossi, GD Förster, C Zeni, J Lam
Carbon Trends 11, 100268, 2023
4 2023 A generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, Z Wang, ...
Nature, 1-3, 2025
3 2025 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
3 2024 
Aldo GlielmoBank of ItalyEmail verificata su bancaditalia.it