The delta-thermostat: selective normal-modes excitation by colored-noise Langevin dynamics. - Michele Ceriotti; Michele Parrinello (ICCS, 2010)

i-PI: A Python interface for ab initio path integral molecular dynamics simulations. - Michele Ceriotti; Joshua More; David E. Manolopoulos (Comput. Phys. Commun., 2014)

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)

Mapping and classifying molecules from a high-throughput structural database. - Sandip De; Felix Musil; Teresa Ingram; Carsten Baldauf; Michele Ceriotti (J. Cheminformatics, 2017)

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; et al. (Comput. Phys. Commun., 2019)

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)

Improving Sample and Feature Selection with Principal Covariates Regression. - Rose K. Cersonsky; Benjamin A. Helfrecht; Edgar A. Engel; Michele Ceriotti (CoRR, 2020)

Chemiscope: interactive structure-property explorer for materials and molecules. - Guillaume Fraux; Rose K. Cersonsky; Michele Ceriotti (J. Open Source Softw., 2020)

Structure-property maps with Kernel principal covariates regression. - Benjamin A. Helfrecht; Rose K. Cersonsky; Guillaume Fraux; Michele Ceriotti (Mach. Learn. Sci. Technol., 2020)

Optimal radial basis for density-based atomic representations. - Alexander Goscinski; Felix Musil; Sergey Pozdnyakov; Michele Ceriotti (CoRR, 2021)

The role of feature space in atomistic learning. - Alexander Goscinski; Guillaume Fraux; Giulio Imbalzano; Michele Ceriotti (Mach. Learn. Sci. Technol., 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., 2021)

Incompleteness of graph convolutional neural networks for points clouds in three dimensions. - Sergey N. Pozdnyakov; Michele Ceriotti (CoRR, 2022)

Unified theory of atom-centered representations and graph convolutional machine-learning schemes. - Jigyasa Nigam; Guillaume Fraux; Michele Ceriotti (CoRR, 2022)

Predicting hot electrons free energies from ground-state data. - Chiheb Ben Mahmoud; Federico Grasselli; Michele Ceriotti (CoRR, 2022)

A smooth basis for atomistic machine learning. - Filippo Bigi; Kevin Huguenin-Dumittan; Michele Ceriotti; David E. Manolopoulos (CoRR, 2022)

Incompleteness of graph neural networks for points clouds in three dimensions. - Sergey N. Pozdnyakov; Michele Ceriotti (Mach. Learn. Sci. Technol., 2022)

Smooth, exact rotational symmetrization for deep learning on point clouds. - Sergey Pozdnyakov; Michele Ceriotti (NeurIPS, 2023)

Fast evaluation of real spherical harmonics and their derivatives in Cartesian coordinates. - Filippo Bigi; Michele Ceriotti (CoRR, 2023)

Completeness of Atomic Structure Representations. - Jigyasa Nigam; Sergey N. Pozdnyakov; Kevin K. Huguenin-Dumittan; Michele Ceriotti (CoRR, 2023)

Wigner kernels: body-ordered equivariant machine learning without a basis. - Filippo Bigi; Sergey N. Pozdnyakov; Michele Ceriotti (CoRR, 2023)

Physics-inspired Equivariant Descriptors of Non-bonded Interactions. - Kevin K. Huguenin-Dumittan; Philip Loche; Haoran Ni; Michele Ceriotti (CoRR, 2023)

Electronic excited states from physically-constrained machine learning. - Edoardo Cignoni; Divya Suman; Jigyasa Nigam; Lorenzo Cupellini; Benedetta Mennucci; Michele Ceriotti (CoRR, 2023)

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; et al. (CoRR, 2024)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning. - Filippo Bigi; Marcel F. Langer; Michele Ceriotti (CoRR, 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)

Uncertainty quantification by direct propagation of shallow ensembles. - Matthias Kellner; Michele Ceriotti (Mach. Learn. Sci. Technol., 2024)

Probing the effects of broken symmetries in machine learning. - Marcel F. Langer; Sergey N. Pozdnyakov; Michele Ceriotti (Mach. Learn. Sci. Technol., 2024)

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, 2025)

Representing spherical tensors with scalar-based machine-learning models. - Michelangelo Domina; Filippo Bigi; Paolo Pegolo; Michele Ceriotti (CoRR, 2025)

FlashMD: long-stride, universal prediction of molecular dynamics. - Filippo Bigi; Sanggyu Chong; Agustinus Kristiadi; Michele Ceriotti (CoRR, 2025)