About PyRETIS¶
PyRETIS is a Python library for rare event molecular simulations with emphasis on methods based on transition interface sampling and replica exchange transition interface sampling. The papers describing the PyRETIS program can be found here: https://doi.org/10.1002/jcc.24900 (PyRETIS 1, 2017), here: https://doi.org/10.1002/jcc.26112 (PyRETIS 2, 2020), and here: https://doi.org/10.1002/jcc.27319 (PyRETIS 3, 2024).
The work on PyRETIS was initiated by Titus van Erp and is used in the research and teaching activities in the theoretical chemistry group at the Norwegian University of Science and Technology.
PyRETIS is open source and is released under a GNU Lesser General Public license v2.1+. If you are interested in contributing to the PyRETIS project, please have a look at the developer guide and visit our git repository https://gitlab.com/pyretis/pyretis.
The current PyRETIS version is 4.0.0.dev0. For an overview of the official PyRETIS releases, please visit our git repository: https://gitlab.com/pyretis/pyretis/-/releases.
Since version 2.4, PyRETIS includes PyVisA, a program created to facilitate post-processing and data analysis.
The PyRETIS team¶
Head authors & project leaders:
Developers:
Former Developers:
Acknowledgments:
Oda Dahlen
Christopher Daub
Mahmoud Moqadam
César A. Urbina-Blanco
Jocelyne Vreede
Magnus Heskestad Waage
To cite us:¶
When using PyRETIS, or one of our libraries, please cite us!
Software papers:
A. Lervik, E. Riccardi and T. S. van Erp, PyRETIS: A well-done, medium-sized python library for rare events, J. Comput. Chem. 38, 2439-2451 (2017).
E. Riccardi, A. Lervik, S. Roet, O. Aarøen and T. S. van Erp, PyRETIS 2: An improbability drive for rare events, J. Comput. Chem. 41, 370-377 (2020).
O. Aarøen, H. Kiær and E. Riccardi, PyVisA: Visualization and Analysis of path sampling trajectories, J. Comput. Chem. 42, 435-446 (2021).
W. Vervust, D. T. Zhang, A. Ghysels, S. Roet, T. S. van Erp and E. Riccardi, PyRETIS 3: Conquering rare and slow events without boundaries, J. Comput. Chem. 45, 1224-1234 (2024).
Studies based on PyRETIS:
In addition to the software papers above, the following published studies use PyRETIS or PyVisA to sample, analyse or visualise rare events. They include work by members of our group and by other authors building on the software.
T. S. van Erp, M. Moqadam, E. Riccardi and A. Lervik, Analyzing complex reaction mechanisms using path sampling, J. Chem. Theory Comput. 12, 5398-5410 (2016).
M. Moqadam, E. Riccardi, T. T. Trinh, A. Lervik and T. S. van Erp, Rare event simulations reveal subtle key steps in aqueous silicate condensation, Phys. Chem. Chem. Phys. 19, 13361-13371 (2017).
E. Riccardi, O. Dahlen and T. S. van Erp, Fast decorrelating Monte Carlo moves for efficient path sampling, J. Phys. Chem. Lett. 8, 4456-4460 (2017).
M. Moqadam, A. Lervik, E. Riccardi, V. Venkatraman, B. K. Alsberg and T. S. van Erp, Local initiation conditions for water autoionization, Proc. Natl. Acad. Sci. U.S.A. 115, E4569-E4576 (2018).
E. Riccardi, E. C. van Mastbergen, W. W. Navarre and J. Vreede, Predicting the mechanism and rate of H-NS binding to AT-rich DNA, PLoS Comput. Biol. 15, e1006845 (2019).
C. D. Daub, E. Riccardi, V. Hänninen and L. Halonen, Path sampling for atmospheric reactions: formic acid catalysed conversion of SO3 + H2O to H2SO4, PeerJ Phys. Chem. 2, e7 (2020).
E. Riccardi, A. Krämer, T. S. van Erp and A. Ghysels, Permeation rates of oxygen through a lipid bilayer using replica exchange transition interface sampling, J. Phys. Chem. B 125, 193-201 (2021).
S. Roet, C. D. Daub and E. Riccardi, Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning, J. Chem. Theory Comput. 17, 6193-6202 (2021).
A. Lervik, I.-H. Svenum, Z. Wang, R. Cabriolu, E. Riccardi, S. Andersson and T. S. van Erp, The role of pressure and defects in the wurtzite to rock salt transition in cadmium selenide, Phys. Chem. Chem. Phys. 24, 8378-8386 (2022).
D. T. Zhang, E. Riccardi and T. S. van Erp, Enhanced path sampling using subtrajectory Monte Carlo moves, J. Chem. Phys. 158, 024113 (2023).
V. Munizaga and M. L. Falk, The thermodynamic effects of solute on void nucleation in Mg alloys, J. Chem. Phys. 161, 044509 (2024).
K. Wilke, S. Tao, S. Calero, A. Lervik and T. S. van Erp, NaCl dissociation explored through predictive power path sampling analysis, J. Chem. Theory Comput. 21, 4604-4614 (2025).
W. Vervust, D. T. Zhang, E. Riccardi, T. S. van Erp and A. Ghysels, Path sampling challenges in large biomolecular systems: RETIS and REPPTIS for ABL-imatinib kinetics, Biophys. J. 124, 3932-3947 (2025).
Additional theory papers from our group:
T. S. van Erp, D. Moroni and P. G. Bolhuis, A novel path sampling method for the calculation of rate constants, J. Chem. Phys. 118, 7762-7774 (2003).
T. S. van Erp, Reaction rate calculation by parallel path swapping, Phys. Rev. Lett. 98, 268301 (2007).
A. Ghysels, S. Roet, S. Davoudi and T. S. van Erp, Exact non-Markovian permeability from rare event simulations, Phys. Rev. Res. 3, 033068 (2021).
W. Vervust, D. T. Zhang, T. S. van Erp and A. Ghysels, Path sampling with memory reduction and replica exchange to reach long permeation timescales, Biophys. J. 122, 2960-2972 (2023).
D. T. Zhang, L. Baldauf, S. Roet, A. Lervik and T. S. van Erp, Highly parallelizable path sampling with minimal rejections using asynchronous replica exchange and infinite swaps, Proc. Natl. Acad. Sci. U.S.A. 121, e2318731121 (2024).