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

Autor:	Litman Y; Y. Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom., Kapil V; Y. Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.; Department of Physics and Astronomy, University College London, 17-19 Gordon St, London WC1H 0AH, United Kingdom.; Thomas Young Centre and London Centre for Nanotechnology, 19 Gordon St, London WC1H 0AH, United Kingdom., Feldman YMY; School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel., Tisi D; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland., Begušić T; Div. of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., Fidanyan K; MPI for the Structure and Dynamics of Matter, Hamburg, Germany., Fraux G; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland., Higer J; School of Physics, Tel Aviv University, Tel Aviv 6997801, Israel., Kellner M; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland., Li TE; Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, USA., Pós ES; MPI for the Structure and Dynamics of Matter, Hamburg, Germany., Stocco E; MPI for the Structure and Dynamics of Matter, Hamburg, Germany., Trenins G; MPI for the Structure and Dynamics of Matter, Hamburg, Germany., Hirshberg B; School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel., Rossi M; MPI for the Structure and Dynamics of Matter, Hamburg, Germany., Ceriotti M; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Jazyk:	angličtina
Zdroj:	The Journal of chemical physics [J Chem Phys] 2024 Aug 14; Vol. 161 (6).
DOI:	10.1063/5.0215869
Abstrakt:	Atomic-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. (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)
Databáze:	MEDLINE
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