Claudio Zeni

Roche (Switzerland) — Switzerland

Materials Science · Physical Sciences

7h-index422citations14works0.02yr avg
OpenAlex↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Machine Learning in Materials Science(9), Computational Drug Discovery Methods(4), nanoparticles nucleation surface interactions(3), Protein Structure and Dynamics(2), Carbon Nanotubes in Composites(2)

Publications14 total

Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems
Digital Discovery·2023· 6 citedOpen Access
Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis
Carbon Trends·2023· 5 citedOpen Access
DADApy: Distance-based analysis of data-manifolds in Python
Patterns·2022· 39 citedOpen Access
Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library
Faraday Discussions·2022· 6 citedOpen Access
Exploring the robust extrapolation of high-dimensional machine learning potentials
Physical review. B./Physical review. B·2022· 34 citedOpen Access
Ranking the information content of distance measures
PNAS Nexus·2022· 57 citedOpen Access
Modeling and Characterization of the Nucleation and Growth of Carbon Nanostructures in Physical Synthesis
SSRN Electronic Journal·2022Open Access
Data-driven simulation and characterisation of gold nanoparticle melting
Nature Communications·2021· 66 citedOpen Access
Data-driven simulation and characterization of gold nanoparticles melting
2021· 1 citedOpen Access
Compact atomic descriptors enable accurate predictions via linear models
The Journal of Chemical Physics·2021· 25 citedOpen Access
Gaussian process regression for nonparametric force fields
2020· 3 cited
On machine learning force fields for metallic nanoparticles
Advances in Physics X·2019· 41 citedOpen Access
Efficient nonparametric<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-body force fields from machine learning
Physical review. B./Physical review. B·2018· 139 citedOpen Access

Frequent Co-authors

Kevin Rossi(10), Aldo Glielmo(8), Stefano de Gironcoli(5), Francesca Baletto(4), Andrea Anelli(3), Theodoros Pavloudis(2), Joseph Kioseoglou(2), Richard E. Palmer(2), Alessandro Laio(2), Julien Lam(2), Alessandro De Vita(1), Bingqing Cheng(1), Gábor Cśanyi(1), Iuri Macocco(1), Diego Doimo(1), Matteo Carli(1), Romina Wild(1), Maria d’Errico(1), Álex Rodríguez(1), Robert Jones(1)