Michele Ceriotti

According to our database, Michele Ceriotti authored at least 34 papers between 2010 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

How unconstrained machine-learning models learn physical symmetries.

Michelangelo Domina, Joseph William Abbott, Paolo Pegolo, Filippo Bigi, Michele Ceriotti

CoRR, March, 2026

Learning Long-Range Representations with Equivariant Messages.

Egor Rumiantsev, Marcel F. Langer, Tulga-Erdene Sodjargal, Michele Ceriotti, Philip Loche

Trans. Mach. Learn. Res., 2026

2025

Comparing the latent features of universal machine-learning interatomic potentials.

Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti, Sanggyu Chong

CoRR, December, 2025

Representing spherical tensors with scalar-based machine-learning models.

Michelangelo Domina, Filippo Bigi, Paolo Pegolo, Michele Ceriotti

CoRR, May, 2025

PET-MAD, a universal interatomic potential for advanced materials modeling.

Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, Michele Ceriotti

CoRR, March, 2025

FlashMD: long-stride, universal prediction of molecular dynamics.

Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

The dark side of the forces: assessing non-conservative force models for atomistic machine learning.

Filippo Bigi, Marcel F. Langer, Michele Ceriotti

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Probing the effects of broken symmetries in machine learning.

Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti

Mach. Learn. Sci. Technol., 2024

Uncertainty quantification by direct propagation of shallow ensembles.

Matthias Kellner, Michele Ceriotti

Mach. Learn. Sci. Technol., 2024

A prediction rigidity formalism for low-cost uncertainties in trained neural networks.

Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli

Mach. Learn. Sci. Technol., 2024

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants.

Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Silvio Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut

CoRR, 2024

2023

Electronic excited states from physically-constrained machine learning.

Edoardo Cignoni, Divya Suman, Jigyasa Nigam, Lorenzo Cupellini, Benedetta Mennucci, Michele Ceriotti

CoRR, 2023

Physics-inspired Equivariant Descriptors of Non-bonded Interactions.

Kevin K. Huguenin-Dumittan, Philip Loche, Haoran Ni, Michele Ceriotti

CoRR, 2023

Wigner kernels: body-ordered equivariant machine learning without a basis.

Filippo Bigi, Sergey N. Pozdnyakov, Michele Ceriotti

CoRR, 2023

Completeness of Atomic Structure Representations.

Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, Michele Ceriotti

CoRR, 2023

Fast evaluation of real spherical harmonics and their derivatives in Cartesian coordinates.

Filippo Bigi, Michele Ceriotti

CoRR, 2023

Smooth, exact rotational symmetrization for deep learning on point clouds.

Sergey Pozdnyakov, Michele Ceriotti

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Incompleteness of graph neural networks for points clouds in three dimensions.

Sergey N. Pozdnyakov, Michele Ceriotti

Mach. Learn. Sci. Technol., December, 2022

A smooth basis for atomistic machine learning.

Filippo Bigi, Kevin Huguenin-Dumittan, Michele Ceriotti, David E. Manolopoulos

CoRR, 2022

Predicting hot electrons free energies from ground-state data.

Chiheb Ben Mahmoud, Federico Grasselli, Michele Ceriotti

CoRR, 2022

Unified theory of atom-centered representations and graph convolutional machine-learning schemes.

Jigyasa Nigam, Guillaume Fraux, Michele Ceriotti

CoRR, 2022

Incompleteness of graph convolutional neural networks for points clouds in three dimensions.

Sergey N. Pozdnyakov, Michele Ceriotti

CoRR, 2022

2021

Improving sample and feature selection with principal covariates regression.

Rose K. Cersonsky, Benjamin A. Helfrecht, Edgar A. Engel, Sergei Kliavinek, Michele Ceriotti

Mach. Learn. Sci. Technol., September, 2021

The role of feature space in atomistic learning.

Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano, Michele Ceriotti

Mach. Learn. Sci. Technol., 2021

Optimal radial basis for density-based atomic representations.

Alexander Goscinski, Felix Musil, Sergey Pozdnyakov, Michele Ceriotti

CoRR, 2021

2020

Structure-property maps with Kernel principal covariates regression.

Benjamin A. Helfrecht, Rose K. Cersonsky, Guillaume Fraux, Michele Ceriotti

Mach. Learn. Sci. Technol., 2020

Chemiscope: interactive structure-property explorer for materials and molecules.

Guillaume Fraux, Rose K. Cersonsky, Michele Ceriotti

J. Open Source Softw., 2020

Improving Sample and Feature Selection with Principal Covariates Regression.

Rose K. Cersonsky, Benjamin A. Helfrecht, Edgar A. Engel, Michele Ceriotti

CoRR, 2020

Uncertainty estimation by committee models for molecular dynamics and thermodynamic averages.

Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil, Kevin Rossi, Edgar A. Engel, Federico Grasselli, Michele Ceriotti

CoRR, 2020

2019

i-PI 2.0: A universal force engine for advanced molecular simulations.

Venkat Kapil, Mariana Rossi, Ondrej Marsalek, Riccardo Petraglia, Yair Litman, Thomas Spura, Bingqing Cheng, Alice Cuzzocrea, Robert H. Meißner, David M. Wilkins, Benjamin A. Helfrecht, Przemyslaw Juda, Sébastien P. Bienvenue, Wei Fang, Jan Kessler, Igor Poltavsky, Steven Vandenbrande, Jelle Wieme, Michele Ceriotti

Comput. Phys. Commun., 2019

2017

Mapping and classifying molecules from a high-throughput structural database.

Sandip De, Felix Musil, Teresa Ingram, Carsten Baldauf, Michele Ceriotti

J. Cheminformatics, 2017

2016

Beyond static structures: Putting forth REMD as a tool to solve problems in computational organic chemistry.

Riccardo Petraglia, Adrien Nicolaï, Matthew D. Wodrich, Michele Ceriotti, Clémence Corminboeuf

J. Comput. Chem., 2016

2014

i-PI: A Python interface for ab initio path integral molecular dynamics simulations.

Michele Ceriotti, Joshua More, David E. Manolopoulos

Comput. Phys. Commun., 2014

2010

The delta-thermostat: selective normal-modes excitation by colored-noise Langevin dynamics.

Michele Ceriotti, Michele Parrinello

Proceedings of the International Conference on Computational Science, 2010