Knowledge-Reuse Transfer Learning Methods in Molecular and Material
  Science
An Chen Zhilong Wang Karl Luigi Loza Vidaurre Yanqiang Han Simin Ye Kehao Tao Shiwei Wang Jing Gao Jinjin Li
Molecules and materials are the foundation for the development of modern
advanced industries such as energy storage systems and semiconductor devices.
However, traditional trial-and-error methods or theoretical calculations are
highly resource-intensive, and extremely long R&D (Research and Development)
periods cannot meet the urgent need for molecules/materials in industrial
development. Machine learning (ML) methods based on big data are expected to
break this dilemma. However, the difficulty in constructing large-scale
datasets of new molecules/materials due to the high cost of data acquisition
and annotation limits the development of machine learning. The application of
transfer learning lowers the data requirements for model training, which makes
transfer learning stand out in researches addressing data quality issues. In
this review, we summarize recent advances in transfer learning related to
molecular and materials science. We focus on the application of transfer
learning methods for the discovery of advanced molecules/materials,
particularly, the construction of transfer learning frameworks for different
systems, and how transfer learning can enhance the performance of models. In
addition, the challenges of transfer learning are also discussed.