  Deep learning models are gaining popularity and potency in predicting polymer
properties. These models can be built using pre-existing data and are useful
for the rapid prediction of polymer properties. However, the performance of a
deep learning model is intricately connected to its topology and the volume of
training data. There is no facile protocol available to select a deep learning
architecture, and there is a lack of a large volume of homogeneous
sequence-property data of polymers. These two factors are the primary
bottleneck for the efficient development of deep learning models. Here we
assess the severity of these factors and propose new algorithms to address
them. We show that a linear layer-by-layer expansion of a neural network can
help in identifying the best neural network topology for a given problem.
Moreover, we map the discrete sequence space of a polymer to a continuous
one-dimensional latent space using a machine learning pipeline to identify
minimal data points for building a universal deep learning model. We implement
these approaches for three representative cases of building sequence-property
surrogate models, viz., the single-molecule radius of gyration of a copolymer,
adhesive free energy of a copolymer, and copolymer compatibilizer,
demonstrating the generality of the proposed strategies. This work establishes
efficient methods for building universal deep learning models with minimal data
and hyperparameters for predicting sequence-defined properties of polymers.
