Polymer Informatics with Multi-Task Learning

10/28/2020
by   Christopher Künneth, et al.
0

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict the properties of new polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively, particularly when some property dataset sizes are small. Data pertaining to 36 different properties of over 13, 000 polymers (corresponding to over 23,000 data points) are coalesced and supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in specific property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.

READ FULL TEXT

page 5

page 8

page 11

research
03/25/2021

Copolymer Informatics with Multi-Task Deep Neural Networks

Polymer informatics tools have been recently gaining ground to efficient...
research
08/02/2022

Curvature-informed multi-task learning for graph networks

Properties of interest for crystals and molecules, such as band gap, ela...
research
02/11/2021

Sequential Sentence Classification in Research Papers using Cross-Domain Multi-Task Learning

The task of sequential sentence classification enables the semantic stru...
research
05/19/2017

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Numerous deep learning applications benefit from multi-task learning wit...
research
03/15/2021

Multi-task learning for virtual flow metering

Virtual flow metering (VFM) is a cost-effective and non-intrusive techno...
research
09/11/2020

Towards Interpretable Multi-Task Learning Using Bilevel Programming

Interpretable Multi-Task Learning can be expressed as learning a sparse ...

Please sign up or login with your details

Forgot password? Click here to reset