Copertina

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

Salvato in:
Pubblicato in:arXiv.org (Jul 10, 2024), p. n/a
Autore principale: Litman, Yair
Altri autori: Kapil, Venkat, Feldman, Yotam M Y, Tisi, Davide, Begušić, Tomislav, Fidanyan, Karen, Fraux, Guillaume, Higer, Jacob, Kellner, Matthias, Li, Tao E, Pós, Eszter S, Stocco, Elia, Trenins, George, Hirshberg, Barak, Rossi, Mariana, Ceriotti, Michele
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Simulation
Algorithms
Python
Rapid prototyping
Neural networks
Modular structures
Machine learning
Electronic structure
Computer simulation
Accesso online:Citation/Abstract
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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.
ISSN:2331-8422
DOI:10.1063/5.0215869
Fonte:Engineering Database