| Autor: |
Carrillo JY; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States., Parambil V; Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India., Patra TK; Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India.; Center for Atomistic Modelling and Materials Design, IIT Madras, Chennai 600036, India., Chen Z; Polymer Science and Engineering Department, Conte Center for Polymer Research, University of Massachusetts, Amherst, Massachusetts 01003, United States., Russell TP; Polymer Science and Engineering Department, Conte Center for Polymer Research, University of Massachusetts, Amherst, Massachusetts 01003, United States.; Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States., Sankaranarayanan SKRS; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.; Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States., Sumpter BG; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States., Batra R; Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India.; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.; Center for Atomistic Modelling and Materials Design, IIT Madras, Chennai 600036, India. |
| Abstrakt: |
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. |
