Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science

Understand

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.