Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, and models. They can target various definitions, distributions, and correlations of concerned physical parameters in given polymer systems, and have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries, thus facilitating mechanism exploration, target prediction, high‐throughput screening, optimization, and rational and inverse designs. This article summarizes representative progress in the recent two…

• NN
  National Natural Science Foundation of China

  Awards: 22173094, 52293471, 52303121
• GU
  Guizhou University
• NK
  National Key Research and Development Program of China

  Awards: 2021YFB3801500, 2022YFB3707303