Autopack
rotates crystal structures in 3D space to minimize their molecules’ projected
area. After convergence, it is possible to extract the crystal’s associated
packing motif based on relative interplanar angles. In this example, the stacks
found after the optimization procedure indicate the structure’s beta packing
motif. Credit: Lawrence Livermore National Laboratory.
Whether
organic chemists are working on developing new molecular energetics or creating
new blockbuster drugs in the pharmaceutical industry, each is searching how to
optimize the chemical structure of a molecule to attain desired target
properties.
Part of
that optimization includes a molecular crystal's packing motif, a perceived
pattern in how molecules orient relative to one another within a crystal
structure. The current packing motif datasets have remained small because of
intensive manual labeling processes and insufficient labeling schemes.
To help
solve this problem, a team of Lawrence Livermore National Laboratory (LLNL)
materials and computer scientists have developed a freely available package,
Autopack, which formalizes the packing motif labeling process and can
automatically process and label the packing motifs of thousands of molecular
crystal structures. The research appears in the Journal of Chemical Information
and Modeling.
Small-scale
crystal engineering studies over the past 30 years suggest that, while
predicting experimental crystal structures from a chemical structure alone
remains out of reach, there may be relationships between molecules' chemical
structures and a specific attribute of the crystal structure they adopt called
the packing motif.
A
molecular crystal's packing motif is an important concept for energetics and
organic electronics applications due to observed correlations between molecular
crystals' packing motifs and performance properties of interest, which include
insensitivity for molecular explosives and charge transport for molecular
semiconductors.
No
formalized and open-source method of assigning packing motifs has ever been
created until now. Instead, packing motifs are ascribed to molecular crystals
simply by human evaluation of a crystal structure and judgment, resulting in
small and noisy datasets.
"In
the era of machine learning, the ability to create large, labeled datasets of
molecular crystal packing motifs is now especially important," said LLNL
data scientist Donald Loveland, lead author of the paper. "Such efforts
may generate models that can predict packing motifs from molecules' chemical
structure alone, which would help organic chemists prioritize syntheses of new
molecules based on the desired packing motif and properties."
The new
LLNL work uses an efficient optimization algorithm that circumvents many
problems found in previously proposed packing motif labeling methods, leading
to new state-of-the-art results when tested on an LLNL-curated dataset.
Through
Autopack, researchers have been able to generate a dataset of nearly 10,000
packing motifs for a set of energetic and energetic-like molecules of interest
to the Lab, a task that would have been impossible before. For context, previous
literature has remained capped on the order of 100 molecules due to the tedious
and time-consuming nature of hand labeling. Early analysis of this new dataset
hints at complex trends between intermolecular interactions, 3-D molecular
conformations and adopted packing motifs currently unexplored in the field,
providing guidance on next steps for crystal engineering pipelines.
The code
is freely available through the Lab's Innovations and Partnerships Office.