Machine Learning Models and Dimensionality Reduction for Prediction of Polymer Properties

Accurate prediction of block polymer properties as a
function of
monomer sequence is necessary for better material development. The
number of permutations of chemistry and sequence is nearly infinite,
and new methods are needed to predict and engineer properties as a
function of molecular structure. In this work, we present a machine
learning approach to determine polymer properties where a feed-forward
neural network is trained to predict the period length of a diblock
lamellar system as a function of block sequence and interaction parameters.
These sequenced polymers are similar to experimentally explored polypeptoid
systems. Additionally, we report on our efforts to explore dimensionality
reduction as a method for gaining physical insights into these polymeric
materials.