Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session T16: Machine Learning and Data in Polymer Physics I
11:30 AM–2:30 PM,
Thursday, March 17, 2022
Room: McCormick Place W-184A
Sponsoring
Units:
DPOLY DBIO DCOMP GDS
Chair: Jonathan Whitmer, University of Notre Dame
Abstract: T16.00007 : Briding the Scale-Gap: Transfer Learning for Fudamental Polymer Properties using Molecular-Dynamics Simulation Data
12:42 PM–12:54 PM
Presenter:
Umi Yamamoto
(Advanced Materials Research Labs., Toray Industries, Inc.)
Authors:
Umi Yamamoto
(Advanced Materials Research Labs., Toray Industries, Inc.)
Masahiro Kitabata
(Advanced Materials Research Labs., Toray Industries, Inc.)
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.
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