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In this work, we present a method to generate a configurational levelfingerprint for polymers using the Bead-Spring-Model. Unlike some of theprevious fingerprinting approaches that employ monomer-level information whereatomistic descriptors are computed using quantum chemistry calculations, thisapproach incorporates configurational information from a coarse-grained modelof a long polymer chain. The proposed approach may be advantageous for thestudy of behavior resulting from large molecular weights. To create thisfingerprint, we make use of two kinds of descriptors. First, we calculatecertain geometric descriptors like Re2, Rg2 etc. and label them as CalculatedDescriptors. Second, we generate a set of data-driven descriptors using anunsupervised autoencoder model and call them Learnt Descriptors. Using acombination of both of them, we are able to learn mappings from the structureto various properties of the polymer chain by training ML models. We test ourfingerprint to predict the probability of occurrence of a configuration atequilibrium, which is approximated by a simple linear relationship between theinstantaneous internal energy and equilibrium average internal energy.

Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine Learning