Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science

TL;DR

This review discusses recent transfer learning advances in molecular and materials science, highlighting how it reduces data needs and improves model performance for discovering new molecules and materials.

Contribution

It provides a comprehensive summary of transfer learning frameworks and their applications in molecular and materials research, addressing current challenges.

Findings

Transfer learning reduces data requirements for molecular/materials modeling.

Transfer learning frameworks enhance model performance in molecule/material discovery.

Challenges in applying transfer learning are identified and discussed.

Abstract

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.