GSoC 2023 Project Ideas
Hello, and welcome to MDAnalysis!
Please read our blog post for important official information.
Please see our Google Summer of Code wiki page for some general information, including advice on application writing and also see our GSoC FAQ for commonly asked questions.
If you just found out about the MDAnalysis Python package from the GSoC website, you can have a look at the MDAnalysis 2021 Trailer [YouTube] to get an overview of the scope of the MDAnalysis package.
MDAnalysis is a Python library for the analysis of computer simulations of many-body systems at the molecular scale, spanning use cases from interactions of drugs with proteins to novel materials. Therefore, our GSoC projects usually require a basic knowledge and hands-on experience of molecular dynamics simulations and the associated analyses, or equivalent experience in simulations and modelling of molecular systems (physics, biophysics, chemistry, or materials). For our suggested projects, please check carefully the project descriptions to see the associated desirable skills.
If you are interested in taking part, please do get in touch on the GSoC with MDAnalysis list. Given the GSoC program structure (medium and long projects), letting us know of your intentions to apply and getting acquainted with the project early will be very helpful.
MDAnalysis welcomes new mentors, please do get in touch on the developer list if you are interested in taking part. We typically expect mentors to be familiar with our development process as evidenced by contributions to the code base and interactions on the developer mailing list.
A list of projects ideas for Google Summer of Code 2023.
The currently proposed projects are:
- Generalise groups
- Extend MDAnalysis interoperability
- Benchmarking and performance optimization
- Transport property calculations
- Implementation of parallel analysis framework
Or work on your own idea! Get in contact with us to propose an idea and we will work with you to flesh it out into a full project. Raise an issue in the Issue Tracker or contact us via the GSoC with MDAnalysis Google group.
Look at the list of all available mentors for MDAnalysis for potential mentors for your project. Please send all communications to the mailing list (and don't contact mentors privately). You can certainly ask for the opinion of a specific mentor if you know that their expertise is particularly suitable for your project.
The table summarizes the project ideas; long descriptions come after the table (or click on the links under each project name). The difficulty is a somewhat subjective ranking, where easy means that we know pretty much what needs to be done, medium requires some additional research into best solutions as part of the project, and hard is high risk/high reward where we think a solution exists but we will have to work with the student to find it and implement it. The project size is either 175 h (medium) or 350 h (long) projects.
| project | name | difficulty | project size | description | skills | mentors |
|---|---|---|---|---|---|---|
| 1 | Generalise Groups | hard | 350 hours | Generalise concept of groups | Python, NetworkX, Molecular modelling | @fiona-naughton, @richardjgowers, @yuxuanzhuang, @RMeli |
| 2 | Extend MDAnalysis Interoperability | medium | 350 hours | Extend converters module to other relevant packages | Python, Molecular Modelling | @fiona-naughton, @hmacdope, @yuxuanzhuang, @RMeli |
| 3 | Benchmarking and performance optimization | medium/hard | 175 hours | write benchmarks for automated performance analysis and address performance bottlenecks | Python, Molecular Modelling | @hmacdope, @orbeckst, @RMeli, @ianmkenney |
| 4 | Transport property calculations | medium | 350 hours | write analysis code to calculate physical transport properties | Python, Physics/Mathematics | @orionarcher, @hmacdope |
| 5 | Implementation of parallel analysis framework | hard | 350 hours | implement parallel framework in MDAnalysis | Python, Parallel Programming, Molecular Modelling | @yuxuanzhuang, @orbeckst, @RMeli, @ianmkenney |
It is common to want to consider a group of atoms as a single site/particle, for example defining the position of a water molecule (or a larger solvent) as its center of mass.
It then follows that it is useful to consider many such groupings as an array of quasi-particles, leading to something like an AtomGroup-Group, e.g. a Group representing a solvent where each item in the Group is a single molecule.
The goal of this project is to make two such groupings, BeadGroup and RingGroup:
-
BeadGroup: groups of atoms that can be represented as a single site/particle. This could be used for analysis purposes, as well as to define coarse-grained beads. -
RingGroup: aromatic rings (eg benzene, nucleobases etc.) can be defined by their position (the geometric center of the ring) and their normal vector (the direction they are facing). This class would be implemented as a special case ofBeadGroupwhich also defines a directionality.
- Design and implement a
BeadGroupclass to represent a container of many groupings of atoms - Generalise existing methods (e.g. center_of_mass) to
BeadGroup - Implement
RingGroup, as a special case ofBeadGroup - Implement ring finding functions to quickly define these groups
- Implement basic
RingGroupanalysis functions, eg angle between rings, π-stacking identification.
- Python
- Graph theory (eg the NetworkX package)
- Molecular modelling
- Chemistry
- https://github.com/MDAnalysis/mdanalysis/issues/1861
- https://github.com/MDAnalysis/mdanalysis/issues/2188
- @richardjgowers
- @fiona-naughton
- @RMeli
- @yuxuanzhang
MDAnalysis has been pushing towards interoperability objectives. In pursuit of this aim, we have already added converters to the ParmEd and RDKit libraries. We aim to continue this direction by focusing on other relevant packages such as MDTraj, pytraj, OpenBabel, and Psi4.
- Create converter classes to and from MDAnalysis to your chosen package(s)
- Python
- Any other language relevant to your chosen package (likely C++)
- Basic knowledge of the chosen package(s)
- Molecular Dynamics/Cheminformatics/Quantum Chemistry (depending on the chosen package)
- @fiona-naughton
- @hmacdope
- @RMeli
- @yuxuanzhuang
The performance of the MDAnalysis library is assessed by automated benchmarks with ASV. The benchmarks are publicly available and are updated every night.
The goal of this project is to increase the performance assessment coverage and identify code that should be improved.
- Write benchmark cases.
- Analyze the performance history to identify code that needs to be improved.
- Optimize the code for at least one of the discovered performance bottlenecks.
- Python
- @orbeckst
- @hmacdope
- @RMeli
- @ianmkenney
Diffusivity and conductivity are key properties of many molecular systems. MDAnalysis currently lacks a way to easily calculate these properties.
- Implement self-diffusivity coefficient calculations
- Implement conductivity calculations with Nernst-Einstein and Green-Kubo methods
- Python
- Mathematics/Physics
- @orionarcher
- @hmacdope
Accelerating the speed of trajectory analysis is crucial with the increasing accessibility of exascale computational resources. MDAnalysis has, since v2.0, implemented serialization protocol for almost all the fundamental components. However, we still lack a basic framework to implement a seamless way to convert current analysis methods into parallel-ready.
- Refactor existing methods from
pmdainto AnalysisBase - Design code structure to accommodate different parallel engine backends e.g. dask, joblib, mpi?
- Guess function to get the optimal number of blocks/cores
- Python
- Parallel Programming
- Chemistry/Molecular Dynamics, Molecular modelling
- @yuxuanzhuang
- @orbeckst
- @RMeli
- @ianmkenney