Polymer Structure Prediction from First Principles

Developing
a large database of polymers structures and properties,
for which suitable polymer structural models are a prerequisite, is
critical for polymer informatics. We present a simple strategy, referred
to as polymer structure predictor (PSP), for predicting the crystal
structural models of linear polymers, given their chain-level atomic
connectivity information. The PSP, which was designed specifically
for polymers, relies on properly defining and sampling the configuration
space. Using this approach, we have successfully recovered eight known
crystal structures of six common linear polymers, including polyethylene,
polyvinylidene fluoride, poly­(vinyl chloride), poly­(p-phenylene sulfide), polyacrylonitrile, and poly-2,5-benzoxazole,
while discovering some new stable structures of three of them, i.e.,
polyvinylidene fluoride, polyacrylonitrile, and poly­(p-phenylene sulfide). The PSP is very simple, highly scalable, suitable
for automatic workflows, and comparable to the best major structure
prediction method in terms of efficiency in polymer crystal structure
prediction. Although challenges in comprehensively accounting for
possible chain-level conformations remain, the PSP will be very useful
in efficiently generating polymer data and strengthening the emerging
polymer informatics ecosystems.