i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations

TL;DR

i-PI 3.0 is a versatile, optimized framework for large-scale atomistic simulations that integrates machine-learning potentials, new features, and improved efficiency for advanced modeling tasks.

Contribution

This release introduces optimized benchmarking, new features like exchange modeling, uncertainty quantification, and enhanced integration capabilities for atomistic simulations.

Findings

Optimized performance for systems up to tens of thousands of atoms.

Implemented algorithms for bosonic and fermionic exchange.

Added uncertainty quantification framework for machine-learning potentials.

Abstract

Atomic-scale simulations have progressed tremendously over the past decade, largely due 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 deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.