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Star block copolymers (s-BCPs) have potential applications as novelsurfactants or amphiphiles for emulsification, compatbilization, chemicaltransformations and separations. s-BCPs are star-shaped macromoleculescomprised of linear chains of different chemical blocks (e.g., solvophilic andsolvophobic blocks) that are covalently joined at one junction point. Variousparameters of these macromolecules can be tuned to obtain desired surfaceproperties, including the number of arms, composition of the arms, and thedegree-of-polymerization of the blocks (or the length of the arm). This makesidentification of the optimal s-BCP design highly non-trivial as the totalnumber of plausible s-BCPs architectures is experimentally or computationallyintractable. In this work, we use molecular dynamics (MD) simulations coupledwith reinforcement learning based Monte Carlo tree search (MCTS) to identifys-BCPs designs that minimize the interfacial tension between polar andnon-polar solvents. We first validate the MCTS approach for design of small-and medium-sized s-BCPs, and then use it to efficiently identify sequences ofcopolymer blocks for large-sized s-BCPs. The structural origins of interfacialtension in these systems are also identified using the configurations obtainedfrom MD simulations. Chemical insights on the arrangement of copolymer blocksthat promote lower interfacial tension were mined using machine learning (ML)techniques. Overall, this work provides an efficient approach to solve designproblems via fusion of simulations and ML and provide important groundwork forfuture experimental investigation of s-BCPs sequences for various applications.

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