18 May 2023
Is your organization interested in expanding its teaching and outreach activities? Are you looking to reach a wider audience or demonstrate how your organization’s software/tools fit into a larger research workflow? MDAnalysis is seeking partnerships with organizations to join forces and offer online teaching workshops for complementary skills between Summer 2023 and Winter 2024.
We welcome remote workshop ideas focusing on a variety of topics around the use of MDAnalysis and complementary packages. Proposed workshops may range from 1-2 hour webinars (for general introductory workshops) to half- or full-day online workshops diving into specific areas of interest. For inspiration, check out our BioExcel webinar (MDAnalysis: Interoperable analysis of Python biomolecular simulations in Python), PRACE/SURF course, (Using MDAnalysis for Efficient Simulation Pre- and Post-Processing), and our CCPBioSim/CCP5 workshops (MDAnalysis and Machine Learning for Molecular Simulations) offered at the University of Edinburgh and Durham University.
Please indicate your interest to partner with us or suggest a workshop, or even just to set up a time to discuss further, through our Google form. The MDAnalysis team will review forms as they are received and follow-up with you as soon as possible. If you have a rough idea of when you would like to offer a workshop, please reach out to us by the following deadlines:
| Proposed Workshop Date |
Deadline |
| July-September 2023 |
June 30, 2023 |
| October-December 2023 |
September 1, 2023 |
| January-March 2024 |
December 1, 2023 |
| April-June 2024 |
March 1, 2023 |
| July-September 2024 |
May 31, 2023 |
| October-December 2024 |
August 30, 2023 |
16 May 2023
We are happy to announce that MDAnalysis is hosting two GSoC students this year - @xhgchen and @marinegor. MDAnalysis has been accepted as its own organization with GSoC for the fourth year running, and we are grateful to Google for granting us the opportunity to get started on two very exciting GSoC projects!
Xu Hong Chen: Add calculations for self-diffusivity and viscosity
Transport properties are values defining mass, momentum, heat, or charge transfer and are vital to biomolecular research and chemical engineering. MDAnalysis currently provides little support for these calculations and adding them to MDAnalysis would greatly benefit researchers and engineers. Xu Hong will implement a class to calculate self-diffusivity via the Green-Kubo method and utility functions to compute shear viscosity via the Einstein and Green-Kubo methods. He will test and revise his algorithms rigorously to ensure they are accurate and efficient. To maximize user accessibility, Xu Hong will write clear and understandable documentation linked to the best practices in the literature. Xu Hong’s project page can be found here.
Xu Hong is an incoming computer science student at the University of British Columbia and a biochemistry graduate from the University of Alberta. He has 2 years of experience in molecular dynamics from his research on the molecular mechanism of Hsp90 in the Spyracopoulos Lab. He is interested in scientific software, high-performance computing, computational research, and open-source software. To rest and relax, Xu Hong enjoys playing piano and listening to Chopin, Schubert, and Bach.
Xu Hong is on GitHub as @xhgchen and on LinkedIn as Xu Hong Chen. He will be sharing his experience on his blog.
Egor Marin: Implementation of parallel analysis in MDAnalysis
Even though MDAnalysis (since 2.0) allows for serialization of almost all fundamental components, it still lacks a seamless way to run analysis of trajectories in a parallel fashion. For his GSoC project, Egor will be implementing a parallel-ready backend for MDAnalysis, hoping that it will increase the speed and accessibility of large-scale molecular dynamics analysis.
Egor will hopefully graduate this year fom the PhD program of the University of Groningen (Netherlands). His main expertise lies in structural biology, as can be seen by his work during his bachelor’s and master’s. Apart from that, he does a lot of computer administration work, and enjoys occasional bouldering and snowboarding.
You can find Egor on GitHub and Twitter. You can follow Egor’s GSoC work throughout the summer on his blog.
— @hmacdope @orionarcher @yuxuanzhuang @RMeli @orbeckst @richardjgowers @ianmkenney @IAlibay @jennaswa (mentors and org admins)
20 Apr 2023
We’re happy to announce that the SolvationAnalysis Python package is now published in the Journal of Open Source Software (JOSS):
Orion Archer Cohen, Hugo Macdermott-Opeskin, Lauren Lee, Tingzheng Hou, Kara D. Fong, Ryan Kingsbury, Jingyang Wang, and Kristin A. Persson, (2023). SolvationAnalysis: A Python toolkit for understanding liquid solvation structure in classical molecular dynamics simulations. Journal of Open Source Software, 8(84), 5183, https://doi.org/10.21105/joss.05183
Originally developed by @orionarcher as a GSoC 2021 project, SolvationAnalysis builds on MDAnalysis and pandas to make analyzing solvation structure much, much easier. With a few lines of code, you can extract key solvation properties from any molecular dynamics simulation. With a few more, you can visualize solvation trends within and between different solutions. Further, SolvationAnalysis is designed with extensibility in mind, exposing a core representation of solvation that users can use for brand new analyses. To get started with using SolvationAnalysis in your workflow, check out the documentation and tutorials.
