GSoC Report: Solvation Analysis

Motivation

My research seeks to discover new materials for battery electrolytes. In particular, I use molecular dynamics and ab initio methods to computationally probe new electrolyte compositions. In my work, a core challenge is scaling up. A molecular dynamics trajectory is a rich and complex data structure, thorough analysis requires interactive visualization and data labeling. Automating this sort of interactive analysis is difficult, but to scale up to hundreds or thousands of electrolytes, it needs to be done.

I applied for Google Summer of Code because I felt that MDAnalysis was the best available tool for the automated analysis of MD trajectories. My aim was to build an abstraction around solvation that would make writing automated analysis methods much easier. Over the past few months, I’ve done that. I am now working to refine the API and implement new methods to extract more information from MD simulations. In time, this will fit into a broader workflow that will enable the autonomous exploration of novel electrolytes.

Retrospective

At the outset of my GSoC project, I hoped to create a package with:

• Functions for selecting the solvation shells of ions.
• Automated analysis of ion coordination environment.
• Documentation, testing, and well-written code.

After 10 weeks of working with the MDAnalysis team, the solvation_analysis package vastly exceeds that original intent. There is a robust and simple API, the functionality generalizes to non-ionic systems, solvation distances are automatically identified, and it’s really fast. Further, I’ve learned a huge amount about software development, the package release cycle, and the world of open-source.

In this post, I will summarize my contributions to solvation_analysis and several features of the package.

My Summer in Pull Requests

Template for a snazzy package

PR #7 and #20

My summer began with a duo of pull requests. PRs #7 and #20 filled out the template generated by the MolSSI cookiecutter, creating a fashionable scaffold that my code could fit into. In essence, the PRs set up the trappings of a real package, assuring users (and myself) of a certain core competence in the development.

Testing, testing, one-two-three

PR #6, #16, and #17

Robust testing is part of a finished package. PR #6 introduced the initial code base and some rudimentary testing. In PR #16, I started using pytest, which made writing and using tests much easier. Finally, PR #17 integrated testing with GitHub CI, finalizing a critical component of my development workflow.

Cutting off radial distribution functions

PR #25

PR #25 was a science challenge in addition to a software challenge. I needed to robustly identify the first minima of noisy RDF data so I could automate the solvation analysis. My approach was to perform a polynomial interpolation of the RDF and then take the 2nd-derivative to find the minima. This worked well for smooth RDFs but failed for RDFs with noise. As a quick fix, I added in several heuristics to get the solvation distances identified on my noisy test data. For now, the RDF parser works reasonably well for smooth data and warns users when it fails. An interpolation of an RDF is shown below.

Analysis: the mega-PR

PR #29

PR #29 is responsible for the majority of functionality in solvation_analysis. It implemented the Solution class and all of the analysis functions, reaching 250 comments from myself and my mentors. Much of the discourse focused on iterating through API designs, which eventually led to a very clean Solution-centered package. The underlying data structure uses the ‘tidy data’ concept, implementing a tidy Pandas.DataFrame that allows analyses to be easily vectorized. As shown below, the DataFrame is indexed by step in the analysis (frame), the solvated_atom from the solute, and the solvent atoms atom_id. This solution provides a unique identifier for each atom participating in solvation.

In pursuit of user number one

PR #36

PR #36 introduced a tutorial to walk users through the basic functionality of solvation_analysis and how to respond to common errors. If you are interested in using the solvation_analysis, this is definitely the best place to start!

Release and versioning

PR #37

I was shocked by how easy it is to release a package to PyPI. For this PR, I learned to use versioneer and twine for Python release management. With just a few terminal commands and file edits, I was able to set up a functioning release management system and put out version 0.1.1.

Solvation Analysis in Practice

Since solvation analysis is up on PyPI, it can be installed with a pip!

>>> pip install solvation_analysis

As in any MDAnalysis workflow, we start by importing the package and instantiating an MDAnalysis Universe.

# imports
import MDAnalysis as mda

# define paths to data
data = "../solvation_analysis/tests/data/bn_fec.data"
traj = "../solvation_analysis/tests/data/bn_fec_short_unwrap.dcd"

# instantiate Universe
u = mda.Universe(data, traj)

Next, we need to define the solute and solvents of interest. The example system is a LiPF₆ electrolyte with BN and FEC as solvents. Here, we treat the Li⁺ ion as the solute and all other molecules as solvents.

# define solute AtomGroup
li_atoms = u.atoms.select_atoms("type 22")

# define solvent AtomGroups
PF6 = u.atoms.select_atoms("byres type 21")
BN = u.atoms.select_atoms("byres type 5")
FEC = u.atoms.select_atoms("byres type 19")

Once we have the universe, solute, and solvents defined, it is quite simple to instantiate and run a solution! Solution, like all MDA analysis classes, subclasses the AnalysisBase class and requires a Solution.run() call to initiate the analysis.

# instantiate solution
from solvation_analysis.solution import Solution

solution = Solution(li_atoms, {'PF6': PF6, 'BN': BN, 'FEC': FEC})

solution.run()

Over the course of a few seconds, the solvation radii and the solvation shell of each ion are automatically identified and stored in a pandas.DataFrame (as described above). We can then examine how well the solvation radii were characterized by calling solution.plot_solvation_radius('<<solvent name>>'). For example,

# we just need this to display our plot
import matplotlib.pyplot as plt

solution.plot_solvation_radius('BN')
plt.show()

Now that we have a completed solution, we can easily print the ion pairing, coordination numbers, and solvation radii.

>>> solution.pairing.pairing_dict
{'BN': 1.0, 'FEC': 0.21, 'PF6': 0.12}
>>> solution.coordination.cn_dict
{'BN': 4.33, 'FEC': 0.25, 'PF6': 0.12}
>>> solution.radii
{'PF6': 2.60, 'BN': 2.61, 'FEC': 2.43}

We can see that the Li⁺ ions coordinate most strongly with BN. The pairing shows that 100% of Li⁺ are coordinated with BN and on average there are 4.33 BN coordinated to each Li⁺. Similar information is available for FEC and PF₆.

We can also use solvation_analysis and a simple Pandas operation to identify the most common solvation shell compositions and find examples of them for visualization. Below, we are using the speciation class to find the most common compositions of solvation shells and then filtering out all shells with a frequency less than 2%.

solution.speciation.speciation_percent.query("count > 0.02")

We can pick out a specific examples of that shell for visualization. First we find all shells with a given composition.

# find all shells matching the given dictionary
solution.speciation.find_shells({'BN': 4, 'FEC': 0, 'PF6': 1})

Then we select the solvation shell and use our favorite visualization package to view it!

# get the shell atoms
atoms = solution.solvation_shell(6, 0)

# visualization is not covered here. I suggest nglview!
visualize(atoms)

Using that approach, you can find all the common solvation shells and generate visualizations for each. For example, see this speciation plot of the BN:FEC system from the example. I’ve plotted the fraction of each solvation shell at two temperatures and then visualized examples of those shells in the margin.

Together, the tools described above are a powerful and convenient workflow for analyzing the solvation structure of a liquid. Several analyses are pre-implemented and the core solvation data structure makes it easy to create new methods. I sincerely hope that it is useful to the community!

The Future

PR #39 and #40

While GSoC has ended, development of solvation_analysis has not. PR #39 adds solvent correlation analysis, statistics on uncoordinated solvents, and a new Valency class. PR #40 introduces a tutorial on interactive visualization. These are all features I am excited for and look forward to developing. As a teaser, I’ll include a correlation plot below:

Acknowledgements

As a whole, the MDAnalysis was incredibly welcoming and helpful. I am genuinly inspired by the dedication and competence of the core developers of the package. I am especially grateful for the help of @hmacdope (Hugo), @richardjgowers (Richard), and @IAlibay (Irfan), who were excellent mentors and are generally talented and kind individuals.

I am also thankful for Google’s generous support of open source software.

It has been an amazing ride 🎢

– @orioncohen