An informatics framework for the design of sustainable, chemically recyclable, synthetically accessible, and durable polymers

We present a novel approach to designing durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs digital reactions using virtual forward synthesis (VFS) to generate over 7 million ROP polymers and machine learning techniques to rapidly predict thermal, thermodynamic, and mechanical properties crucial for performance and recyclability. This methodology enables the generation and evaluation of millions of hypothetical ROP polymers from known and commercially available molecules, guiding the selection of approximately 35,000 candidates with optimal features for sustainability and utility. Three of these recommended candidates have passed validation tests in the physical lab — two of the three by others, as published previously elsewhere, and one of them is a new thiocane polymer synthesized, tested, and reported here. This paper highlights the potential of VFS and machine learning to enable a large-scale search of the polymer universe and advance the development of recyclable and environmentally benign polymers.