Claudio Zeni
Claudio Zeni
Senior Researcher @ Microsoft
Verified email at
Cited by
Cited by
Efficient nonparametric -body force fields from machine learning
A Glielmo, C Zeni, A De Vita
Physical Review B 97 (18), 184307, 2018
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
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
On machine learning force fields for metallic nanoparticles
C Zeni, K Rossi, A Glielmo, F Baletto
Advances in Physics: X 4 (1), 1654919, 2019
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
Ranking the information content of distance measures
A Glielmo, C Zeni, B Cheng, G Csányi, A Laio
PNAS nexus 1 (2), pgac039, 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
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
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
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
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
Gaussian process regression for nonparametric force fields
C Zeni
King's College London, 2020
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
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
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
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
Atomistic fracture modelling by inference-boosted first-principles techniques
A Glielmo, C Zeni, M Caccin, A De Vita
14th International Conference on Fracture, ICF 2017, 2017
King’s Research Portal
KC Mei, N Rubio, PM Coutinho Da Costa, H Kafa, V Abbate, F Festy, ...
Chem. Commun 51, 14981, 2015
Young Researcher’s Workshop on Machine Learning for Materials Science
M Todorovic, A Foster, P Rinke, C Zeni, K Rossi, A Glielmo
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