Litman, YairKapil, VenkatFeldman, Yotam M.Y.Tisi, DavideBegušić, TomislavFidanyan, KarenFraux, GuillaumeHiger, JacobKellner, MatthiasLi, Tao E.Pós, Eszter S.Stocco, EliaTrenins, GeorgeHirshberg, BarakRossi, MarianaCeriotti, Michele2025-01-242025-01-242025-01-242024-08-1410.1063/5.02158692-s2.0-85201252226https://infoscience.epfl.ch/handle/20.500.14299/24359039140447Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.entruetext::journal::journal article::research article