Briding the Scale-Gap: Transfer Learning for Fudamental Polymer Properties using Molecular-Dynamics Simulation Data

Enrichment of databases covering a wide range of molecular structures, materials properties, measurement details, etc. remains as a major challenge in polymer informatics. While molecular dynamics (MD) and other simulations provide a general way to produce arbitrary data with full details of its origin, they often require unrealistically long computational times when macroscopic/product-scale performances must be considered.

In this talk, we will discuss our recent effort to tackle the above challenge by combining MD simulation and transfer learning (TL). As an initial study, MD simulation data is semi-automatically generated for 100+ linear homopolymer melts, which is then utilized as a training set for the “source” task to build a feature encoder for the "target" task. With only 10+ experimental data, TL is able to predict experimental tensile modulus with > 3x higher accuracy compared to the traditional approach that only learns experimental data, demonstrating the usefulness of the proposed approach. This result not only helps apply polymer informatics to real-world problems, but also proposes a way to expand the applicability of theoretical/computational approaches by connecting the scale-gap between the product-scale properties and theoretically accessible quantities.