Liao Vinson, Myers Tristan, Jayaraman Arthi
Department of Chemical and Biomolecular Engineering, University of Delaware, Colburn Lab, 150 Academy Street, Newark, DE 19716, USA.
Department of Materials Science and Engineering, University of Delaware, 201 DuPont Hall, Newark, Delaware 19716, USA.
Soft Matter. 2024 Oct 23;20(41):8246-8259. doi: 10.1039/d4sm01037j.
Tailoring polymers for target applications often involves selecting candidates from a large design parameter space including polymer chemistry, molar mass, sequence, and architecture, and linking each candidate to their assembled structures and in turn their properties. To accelerate this process, there is a critical need for inverse design of polymers and fast exploration of the structures they can form. This need has been particularly challenging to fulfill due to the multiple length scales and time scales of structural arrangements found in polymers that together give rise to the materials' properties. In this work, we tackle this challenge by introducing a computational framework called RAPSIDY - Rapid Analysis of Polymer Structure and Inverse Design strategY. RAPSIDY enables inverse design of polymers by accelerating the evaluation of stability of multiscale structure for any given polymer design (sequence, composition, length). We use molecular dynamics simulations as the base method and apply a guiding potential to initialize polymers chains of a selected design within target morphologies. After initialization, the guiding potential is turned off, and we allow the chains and structure to relax. By evaluating similarity between the target morphology and the relaxed morphology for that polymer design, we can screen many polymer designs in a highly parallelized manner to rank designs that are likely to remain in that target morphology. We demonstrate how this method works using an example of a symmetric, linear pentablock, ABABA, copolymer system for which we determine polymer sequences that exhibit stable double gyroid morphology. Rather than trying to identify the global free-energy minimum morphology for a specific polymer design, we aim to identify candidates of polymer design parameter space that are more stable in the desired morphology than others. Our approach reduces computational costs for design parameter exploration by up to two orders-of-magnitude compared to traditional MD methods, thus accelerating design and engineering of novel polymer materials for target applications.
