Polymer sequence design via molecular simulation-based active learning

Molecular-scale interactions and chemical structures offer an enormous opportunity to tune material properties. However, designing materials from their molecular scale is a grand challenge owing to the practical limitations in exploring astronomically large design spaces using traditional experimental or computational methods. Advancements in data science and machine learning have produced a host of tools and techniques that can address this problem and facilitate the efficient exploration of large search spaces. In this work, a blended approach integrating physics-based methods, machine learning techniques and uncertainty quantification is implemented to effectively screen a macromolecular sequence space and design target structures. Here, we survey and assess the efficacy of data-driven methods within the framework of active learning for a challenging design problem, viz., sequence optimization of a copolymer. We report the impact of surrogate models, kernels, and initial conditions on the convergence of the active learning method for the sequence design problem. This work establishes optimal strategies and hyperparameters for efficient inverse design of polymer sequences via active learning.