Molecular dynamics simulations combined with Gaussian Process regression to investigate block copolymer orientation in thin films

Block copolymer (BCP) thin films are well known to form nanoscale morphologies that are promising as templates and functional materials. The orientation of the morphology is critical for applications and influenced by a host of parameters, including chain architecture, surface tension of each block, and substrate properties. The resulting morphological phase diagram is thus high dimensional and extremely difficult to explore. We deployed molecular dynamics (MD) simulations combined with autonomous experimentation (AE) methods to efficiently explore the vast and complex parameter space of BCP ordering. Our AE pipeline consists of running a MD simulation of BCP chains for a particular point in parameter space, automatically analyzing the results to extract structural metrics, and using an autonomous decision-making algorithm to suggest the next simulation point (the next sets of parameters). This AE loop iterates to build a robust model of the system. The decision-making algorithm is a Gaussian Process regression, customized with a kernel that captures known symmetries of the parameter space.