Polymers are among the most common materials in modern society, with applications ranging from car tires to food packaging to vaccine-stabilizing agents. Given their global impact, the continual evolution of polymers remains a pressing pursuit. Recently, researchers have begun looking towards data science and cheminformatics to accelerate the pace of polymer development. However, polymers are stochastic molecules, having distributions in chain length, composition, and topology. This makes it challenging to describe these molecules with typical molecular representations, and ultimately hinders the expansion of data science into the polymer domain.<br/><br/>To address this challenge, BigSMILES was developed to extend SMILES (simplified molecular-input line-entry system) to handle stochasticity. The major extension made by BigSMILES is the introduction of a stochastic object, which encapsulates all stochastic molecular fragments, and bonding descriptors, which signify how these molecular fragments are joined together. This key addition enables BigSMILES to represent a huge variety of polymers, such as networks, branching, complex monomer sequences, blocky topologies, polymer stereochemistry, and even ladder polymers while maintaining both human and machine readability.<br/><br/>This talk will cover the development of BigSMILES, how it works, and how it has led to CRIPT (Community for Innovation in Polymer Technology). CRIPT seeks to build an open-source digital ecosystem for polymers to accelerate the pace of material discovery.

BigSMILES: Representing the stochasticity of polymers

ACS Spring 2022

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