Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning

Star block copolymers (s-BCPs) have potential applications
as novel
surfactants or amphiphiles for emulsification, compatibilization,
chemical transformations, and separations. s-BCPs have chain architectures
where three or more linear diblock copolymer arms comprised of two
chemically distinct linear polymers, e.g., solvophobic and solvophilic
chains, are covalently joined at one point. The chemical composition
of each of the subunit polymer chains comprising the arms, their molecular
weights, and the number of arms can be varied to tailor the surface
and interfacial activity of these architecturally unique molecules.
This makes identification of the optimal s-BCP design nontrivial as
the total number of plausible s-BCP architectures is experimentally
or computationally intractable. In this work, we use molecular dynamics
(MD) simulations coupled with a reinforcement learning-based Monte
Carlo tree search (MCTS) to identify s-BCP designs that minimize the
interfacial tension between polar and nonpolar solvents. We first
validate the MCTS approach for the design of small- and medium-sized
s-BCPs and then use it to efficiently identify sequences of copolymer
blocks for large-sized s-BCPs. The structural origins of interfacial
tension in these systems are also identified by using the configurations
obtained from MD simulations. Chemical insights into the arrangement
of copolymer blocks that promote lower interfacial tension were mined
using machine learning (ML) techniques. Overall, this work provides
an efficient approach to solve design problems via fusion of simulations
and ML and provides important groundwork for future experimental investigation
of s-BCPs for various applications.