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 ...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.展开更多
基金supported by the Office of Naval Research through a Multidisciplinary University Research Initiative(MURI)Grant(N00014-20-1-2586)J.K.gratefully acknowledges support through the National Defense Science and Engineering(NDSEG)Fellowship Program from the Department of Defense(DoD).J.K.is grateful to Aubrey Toland for his support on the paper.
文摘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.
