Polymer Structure Prediction from First Principles.

Abstract

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.