Projects per year
Abstract
We present JeLLyFysh-Version1.0, an open-source Python application for event-chain Monte Carlo (ECMC), an event-driven irreversible Markov-chain Monte Carlo algorithm for classical
-body simulations in statistical mechanics, biophysics and electrochemistry. The application’s architecture mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules.
-body simulations in statistical mechanics, biophysics and electrochemistry. The application’s architecture mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules.
Original language | English |
---|---|
Pages (from-to) | 107168 |
Journal | Comput. Phys. Commun |
Volume | 253 |
Early online date | 20 Jan 2020 |
DOIs | |
Publication status | Published - Aug 2020 |
Fingerprint
Dive into the research topics of 'JeLLyFysh-Version1.0 - a Python application for all-atom event-chain Monte Carlo'. Together they form a unique fingerprint.Projects
- 2 Finished
-
All-atom computations of long-range interactions with nonreversible Markov chains
Faulkner, M. (Principal Investigator)
1/09/17 → 1/11/18
Project: Research
-
Non-ergodic dynamics and topological-sector fluctuations in layered high-temperature superconductors
Faulkner, M. (Principal Investigator)
1/08/17 → 18/10/23
Project: Research
Datasets
-
JeLLyFysh-Version1.0 - a Python application for all-atom event-chain Monte Carlo
Höllmer, P. (Contributor), Qin, L. (Contributor), Faulkner, M. F. (Contributor), Maggs, A. C. (Contributor) & Krauth, W. (Contributor), Mendeley Data, 2020
DOI: 10.17632/srrjt9493d.1, https://data.mendeley.com/datasets/srrjt9493d
Dataset