摘要
Machine learning(ML)models for predicting gas permeability through polymers have traditionally relied on experimental data.While these models exhibit robustness within familiar chemical domains,reliability wanes when applied to new spaces.To address this challenge,we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques.
基金
supported by Toyota Research Institute through the Accelerated Materials Design and Discovery program and the Office of Naval Research through a multidisciplinary university research initiative(MURI)grant N00014-20-1-2586
This research is supported in part through research cyber-infrastructure resources and services provided by the Partnership for an Advanced Computing Environment(PACE)at the Georgia Institute of Technology,Atlanta,Georgia,USA^(52)
The authors thank XSEDE/ACCESS for computational support through Grant No.TGDMR080058N.
