  Analysis of molecular scale interactions and chemical structure offers an
enormous opportunity to tune material properties for targeted applications.
However, designing materials from molecular scale is a grand challenge owing to
the practical limitations in exploring astronomically large design spaces using
traditional experimental or computational methods. Advancements in data
sciences and artificial intelligence have produced a host of tools and
techniques that can facilitate the efficient exploration of large search
spaces. In this work, a blended approach integrating physics-based methods and
data science techniques is implemented in order to effectively screen the
materials configuration space for accelerating materials design. Here, we
survey and assess the efficacy of data-driven methods within the framework of
active learning for a challenging design problem viz, sequence optimization of
copolymers. We report the impact of surrogate models, kernels, and initial
conditions on the convergence of an active learning method for the sequence
design problem. This work outlines optimal strategies and parameters for
inverse design of polymer sequence via active learning.
