|About this Abstract
||Materials Science & Technology 2020
||Materials Design through AI Composition and Process Optimization
||NEW - Polymer Property Prediction and Design through Multi-task Learning
||Christopher Benjamin Kuenneth, Lihua Chen, Huan Tran, Chiho Kim, Rampi Ramprasad
|On-Site Speaker (Planned)
||Christopher Benjamin Kuenneth
Polymers are an important class of materials that display morphological complexity and diversity spanning a huge property space. Machine learning methods have been recently successfully deployed to explore this unknown polymer property space revealing previously unidentified and novel polymers. The training of machine learning models requires a numerical representation of polymers, commonly termed fingerprints, as inputs which are "mapped" to the polymer properties as outputs. Single-task machine learning models learn the mapping between fingerprints and a single property. Contrarily, multi-task models learn the simultaneous prediction of multiple properties including cross-property correlations. Once trained, multi-task models can not only capture polymer properties but also their correlations which can be extracted and verbalized into polymer design instructions. In this work, we developed a multi-task model for 15 different polymer properties. A comprehensive comparison with single-task models demonstrates superiority of the multi-task model. Moreover, cross-property knowledge is extracted and design instructions are demonstrated.