Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

To discover new materials, high-throughput
screening (HTS) with
machine learning (ML) requires universally available descriptors that
can accurately predict the desired properties. For elastomers, experimental
and simulation data in current descriptors may not be available for
all candidates of interest, hindering elastomer discovery through
HTS. To address this challenge, we introduce structure-based multilevel
(SM) descriptors of elastomers derived solely from molecular structure
that is universally available. Our SM descriptors are hierarchically
organized to capture both local soft and hard segment structures as
well as the global structures of elastomers. With the SM-Morgan Fingerprint
(SM-MF) descriptor, one of our SM descriptors, a machine learning
model accurately predicts elastomer toughness with a remarkable accuracy
of 0.91. Furthermore, an HTS pipeline is established to swiftly screen
elastomers with targeted toughness. We also demonstrate the generality
and applicability of SM descriptors by using them to construct HTS
pipelines for screening elastomers with a targeted critical strain
or Young’s modulus. The user-friendliness and low computational
cost of SM descriptors make them a promising tool to significantly
enhance HTS in the search for novel materials.