Bridging Particle and Field-Theoretic Simulations of Polymers with Deep Learning

Polymer science holds a pivotal role in areas such as advanced materials design, drug delivery systems, and biological systems. In these applications, being able to efficiently predict polymer thermodynamics and self-assembly is crucial. Self-consistent field theory (SCFT) offers a theoretical framework with many successful predictions that have guided experiments. However, there are many examples where SCFT is challenging to apply due to its computational expense, such as systems with broad polydispersity or polymers with semiflexible backbones. Given the intrinsic limitations of SCFT in capturing dynamic behaviors, this study proposes a machine-learning approach that aims to bridge the gap between field-based and particle-based simulations. By integrating convolutional neural networks into SCFT, we aspire to utilize data from particle-based simulations for more informed training and enhanced predictive purposes. This SCFT informed by deep learning (SCFT-DL) strategy not only combines the strengths of both methods but also extends the classes of systems that can be efficiently described by SCFT. In this talk, I will describe how we have integrated CNNs, particle-based simulations, and field-theoretic simulations to extend the range of materials that can be modeled with SCFT. I will compare the efficiency of our method to existing methods and discuss numerous potential future applications for SCFT-DL.