Deep Learning Approaches for Property Prediction and Inverse Design of Polymers

As an emerging paradigm, deep learning has attracted tremendous attention for unveiling molecular structure-property relationships and designing new materials. However, unlike highly crystalline inorganic materials, applying deep learning to polymeric systems is inherently challenged by the polymer’s multi-leveled features and complexity. Here, we present our efforts to apply deep learning models to polymeric systems by proposing an efficient and simplified polymer-to-string representation. We demonstrate that the proposed representation combined with RNN(Recurrent Neural Network) network provides satisfactory performance in predicting bulk properties of polymer melts. Furthermore, our representation is applied for an inverse design network that can generate polymer structures with targeted properties; the conciseness of the proposed representation guarantees the increased validity of output polymer structures.