Group Webinar May 20, 2020: Philipp Höllmer
From Werner KRAUTH
Revision as of 22:29, 20 May 2020 Werner (Talk | contribs) ← Previous diff |
Current revision Werner (Talk | contribs) |
||
Line 1: | Line 1: | ||
- | Group seminar Wednesday, 20 May 2020, 10h30 | + | '''Group Webinar Wednesday, 20 May 2020, 10h30''' |
- | Speaker: Philipp Höllmer | + | '''Speaker:''' '''''Philipp Höllmer (University of Bonn)''''' |
- | Title: JeLLyFysh-Version1.1 — A General-Purpose Python Application for All-Atom Event-Chain Monte Carlo | + | |
- | Reference: https://doi.org/10.1016/j.cpc.2020.107168 | + | '''Title:''' '''''JeLLyFysh-Version1.1 — A General-Purpose Python Application for All-Atom Event-Chain Monte Carlo''''' |
- | Abstract: | + | '''Abstract:''' |
The open-source JeLLyFysh Python application implements the event-chain Monte Carlo algorithm (ECMC) for a wide range of all-atom systems. This talk introduces the application’s architecture, and shows how it systematically formulates the entire time evolution of ECMC in terms of events. A number of worked out simulation examples that use, in particular, the long-ranged Coulomb interaction and the cell-veto algorithm are presented. Finally, recent improvements of the application in its newest version 1.1 are highlighted for the first time. This version marks the next step on the application’s way to a basis code that fosters the development of irreversible Markov-chain algorithm. | The open-source JeLLyFysh Python application implements the event-chain Monte Carlo algorithm (ECMC) for a wide range of all-atom systems. This talk introduces the application’s architecture, and shows how it systematically formulates the entire time evolution of ECMC in terms of events. A number of worked out simulation examples that use, in particular, the long-ranged Coulomb interaction and the cell-veto algorithm are presented. Finally, recent improvements of the application in its newest version 1.1 are highlighted for the first time. This version marks the next step on the application’s way to a basis code that fosters the development of irreversible Markov-chain algorithm. | ||
+ | |||
+ | '''Reference:''' https://doi.org/10.1016/j.cpc.2020.107168 | ||
+ | |||
+ | '''Live Recording:''' https://youtu.be/Io13Mh-gv5w | ||
+ | |||
+ | [[Group_WK#Group_Webinar|Return to Group Webinars]] |
Current revision
Group Webinar Wednesday, 20 May 2020, 10h30
Speaker: Philipp Höllmer (University of Bonn)
Title: JeLLyFysh-Version1.1 — A General-Purpose Python Application for All-Atom Event-Chain Monte Carlo
Abstract: The open-source JeLLyFysh Python application implements the event-chain Monte Carlo algorithm (ECMC) for a wide range of all-atom systems. This talk introduces the application’s architecture, and shows how it systematically formulates the entire time evolution of ECMC in terms of events. A number of worked out simulation examples that use, in particular, the long-ranged Coulomb interaction and the cell-veto algorithm are presented. Finally, recent improvements of the application in its newest version 1.1 are highlighted for the first time. This version marks the next step on the application’s way to a basis code that fosters the development of irreversible Markov-chain algorithm.
Reference: https://doi.org/10.1016/j.cpc.2020.107168
Live Recording: https://youtu.be/Io13Mh-gv5w