JOURNAL ARTICLE
PathSum: A C++ and Fortran suite of fully quantum mechanical real-time path integral methods for (multi-)system + bath dynamics.
Published In: Journal of Chemical Physics, 2023, v. 158, n. 22. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kundu, Sohang; Makri, Nancy 3 of 3
Abstract
This article presents PathSum, a newly released software suite implementing fully quantum mechanical real-time path integral methods for simulating the dynamics of single or extended quantum systems coupled to harmonic environments. The package includes two main modules: a system–bath module for single systems interacting with common or local harmonic baths, and an extended system module designed for chains or aggregates of coupled system–bath units, such as spin chains or molecular aggregates. PathSum incorporates several state-of-the-art algorithms—including the small matrix path integral (SMatPI), iterative quasi-adiabatic propagator path integral (i-QuAPI), blip-summed path integral (BlipSum), time evolving matrix product operator (TEMPO), quantum–classical path integral (QCPI), and modular path integral (MPI) methods—each suited to different parameter regimes and system sizes. The article details the theoretical framework, method implementations, code structure, and guidance for method selection, and illustrates the software's capabilities through six tutorial examples ranging from two-level systems to multistate aggregates and spin chains, highlighting the complementarity and computational advantages of the various approaches within PathSum.
Additional Information
- Source:Journal of Chemical Physics. 2023/06, Vol. 158, Issue 22, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:0021-9606
- DOI:10.1063/5.0151748
- Accession Number:164374100
- Copyright Statement:Copyright of Journal of Chemical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.