Coarse-Grained Configurational Polymer Fingerprints for Property
  Prediction using Machine Learning
Ishan Kumar Prateek K Jha
In this work, we present a method to generate a configurational level
fingerprint for polymers using the Bead-Spring-Model. Unlike some of the
previous fingerprinting approaches that employ monomer-level information where
atomistic descriptors are computed using quantum chemistry calculations, this
approach incorporates configurational information from a coarse-grained model
of a long polymer chain. The proposed approach may be advantageous for the
study of behavior resulting from large molecular weights. To create this
fingerprint, we make use of two kinds of descriptors. First, we calculate
certain geometric descriptors like Re2, Rg2 etc. and label them as Calculated
Descriptors. Second, we generate a set of data-driven descriptors using an
unsupervised autoencoder model and call them Learnt Descriptors. Using a
combination of both of them, we are able to learn mappings from the structure
to various properties of the polymer chain by training ML models. We test our
fingerprint to predict the probability of occurrence of a configuration at
equilibrium, which is approximated by a simple linear relationship between the
instantaneous internal energy and equilibrium average internal energy.