GSoC Ideas 2016

Guidelines

The Open Chemistry project is putting together a proposal for this year's Google Summer of Code. Open Chemistry is an umbrella for projects in chemistry, materials science, biochemistry, and related areas. We intend to concentrate mainly on projects to improve Avogadro 2, cclib, and Open Babel. We have gathered a pool of interested mentors together who are seasoned developers in each of these projects, and would welcome original ideas in addition to those presented here.

Adding Ideas

When adding a new idea to this page, please try to include the following information:

• A brief explanation of the idea.
• Expected results/feature additions.
• Any prerequisites for working on the project.
• Links to any further information, discussions, bug reports etc.
• Any special mailing lists if not the standard mailing list for the project
• Your name and email address for contact (if willing to mentor, or nominated mentor).

Avogadro 2 Project Ideas

Project: Biological Data Visualization

Brief explanation: Support for biological data, representations, and visualization

Expected results: Add support for molecular fragments on top of the molecule model, extending this to residues, and supporting reading/writing this secondary structure (e.g., PDB format). Additional rendering modes for secondary biological structures (i.e. ribbons, cartoons, etc.), building up a biomolecule from residues, and adding residue labels.

Prerequisites: Experience in C++, some experience with OpenGL and an biochemistry ideally, but not necessary.

Mentor: Marcus D. Hanwell (marcus dot hanwell at kitware dot com).

Project: Molecular Dynamics

Brief explanation: Improve support for molecular dynamics simulations in Avogadro 2

Expected results: Initial support is already present, with support for reading in basic trajectories from XYZ files, and static .gro files for GROMACS. Extend this to more fully support the needs of molecular dynamics, reading in trajectory files, ideally loading in time steps on demand for large files rather than loading the entire file in up front. Investigate whether compression techniques (e.g., delta compression) can improve reading and rendering performance. Investigate ways to support generating input, and dealing with extremely large systems (over one million particles). Add support for characterizing particle movement (e.g., pair-wise distribution functions), rare events, and visualizing these in addition to simple trajectory animations.

Prerequisites: Experience in C++, some experience with OpenGL and an MD code ideally, but not necessary.

Mentor: Marcus D. Hanwell (marcus dot hanwell at kitware dot com).

Project: Scripting Bindings

Brief explanation: Implement an embedded scripting language (e.g., Python or JavaScript) in Avogadro 2

Expected results: Create bindings for the C++ libraries in Python or JavaScript / QtScript. This should allow an embedded scripting console as well as support for implementing modular extensions (tools, rendering, etc.) in Python or JavaScript. A Boost.Python implementation existed in Avogadro v1, but has not been re-implemented with the new code base. An ideal solution would connect to QML and Qt to allow scripting to add menu items, windows, etc. and provide documentation and example scripts. The interface should be maintainable as new classes and methods are added.

Prerequisites: Experience in C++ and Python or JavaScript, some experience with SWIG, Boost.Python

Mentor: Geoff Hutchison (geoffh at pitt dot edu)

cclib Project Ideas

cclib is an open source library, written in Python, for parsing and interpreting the results of computational chemistry packages. The goals of cclib are centered around the reuse of data obtained from these programs and contained in output files.

Project: Integrate with Avogadro

Brief explanation: Allow Avogadro to parse cclib-supported formats.

Expected results: Call python scripts to attempt parsing QM file formats with cclib, handle calls into cclib, and convert from Python Objects to C++ objects or to a format supported by Avogadro (e.g, CML or Chemical JSON).

Prerequisites: Experience in C++, some experience with the Python C-bindings ideal, but not necessary.

Mentor: Adam Tenderholt (atenderholt at gmail dot com) and Marcus D. Hanwell (marcus dot hanwell at kitware dot com), possibly Karol Langner (karol.langner at gmail dot com).

Open Babel Project Ideas

Open Babel is an open toolbox for chemistry, designed to speak the many languages of chemical data. It's an open, collaborative project allowing anyone to search, convert, analyze, or store data from molecular modeling, chemistry, solid-state materials, biochemistry, or related areas.

Project: Efficient Parallel Maximum Weighted Matching Graphs

Brief explanation: Improve performance of bond perception (and aromaticity detection) using maximum weighted matching algorithms

Expected results: Currently the performance of ring perception and aromaticity detection in Open Babel is extremely poor, particularly on structures with many fused rings. The current implementation can be exponential in the number of fused rings. Improved implementations exist, mostly using high-performance or parallel implementations of maximum weighted matching graph algorithms from combinatorial optimization. Implementing an improved chemical graph library would dramatically benefit multiple areas of Open Babel.

Prerequisites: Experience in C++, some experience with OpenMP or OpenCL ideally.

Mentor: Geoff Hutchison (geoffh at pitt dot edu).

Project: Fragment-Based Coordinate Generation

Brief explanation: A key problem is generating 3D coordinates for a known molecule. Implement a fragment-based generator to supplement the rule-based algorithm.

Expected results: Currently Open Babel uses a combination of a rule-based approach (i.e., expected geometries) to generate atom-by-atom the 3D coordinates of molecules. Fragments are only used for some ring-based structures. For inorganic and organometallic molecules, the rules may fail. Importantly, the approach is highly inefficient, since fragments can set many atoms at once. The project should generate a library reflecting a balance between efficiency (i.e., many common fragments) and size, as well as an efficient, parallel algorithm for connecting fragments. A knowledge-based fragment approach can also supplement and minimize the need for conformer sampling.

Prerequisites: Experience in C++ and linear algebra. Knowledge of statistics (e.g., Bayesian inference, data mining), OpenMP or OpenCL ideal.

Mentor: Geoff Hutchison (geoffh at pitt dot edu) or David Koes (dkoes at pitt dot edu)

3Dmol.js Project Ideas

3Dmol.js is a modern, object-oriented JavaScript library for visualizing molecular data that is forked from GLmol. A particular emphasis is placed on performance.

Project: Implement volumetric rendering in 3Dmol.js

Brief explanation: Volumetric rendering provides a way to visualize volumetric data in more detail than simple isosurfaces.

Expected results: A number of different volumetric rendering techniques will be implemented and evaluated for a variety of molecular data types.

Prerequisites: Familiarity with JavaScript, WebGl and/or OpenGL, and basic matrix algebra.

Mentor: David Koes (dkoes@pitt.edu)

Project: Molecular Dynamics Visualization

Brief explanation: Implement high-performance in-browser visualization of molecular dynamics simulations.

Expected results: Initial support is already present, with support for reading in basic trajectories from XYZ files, and static .gro files for GROMACS. Extend this to more fully support the needs of molecular dynamics, reading in trajectory files, ideally loading in time steps on demand for large files rather than loading the entire file in up front. Investigate whether compression techniques (e.g., delta compression) can improve reading and rendering performance. Investigate ways to support generating input, and dealing with extremely large systems (over one million particles). Add support for characterizing particle movement (e.g., pair-wise distribution functions), rare events, and visualizing these in addition to simple trajectory animations.

Prerequisites: Experience with JavaScript and client-server programming, some experience with OpenGL/WebGL and an MD code ideally, but not necessary.

Mentor: David Koes l (dkoes@pitt.edu).