| Research Progress of Small Data Machine Learning Algorithms in Materials Science Field |
| Received:November 19, 2024 Revised:November 19, 2024 |
| DOI：10.12136/j.issn.1000-890X.2026.04.0309 |
| Key Words: machine learning；small data learning；learning strategy；active learning；transfer learning |
| Author Name | Affiliation | E-mail | | zhangjiaqiang | School of Materials and Chemical Engineering, Southwest University of Science and Technology | jiaqiangzhang0505@163.com | | LU Ai* | Institute of Chemical Materials, China Academy of Engineering Physics | ai_lu@sina.com | | KANG Ming | Southwest University of Science and Technology | | | YU Fengmei | State Key Laboratory of Environmentally Friendly Energy, | |
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| Abstract: |
| Relying on the traditional experiment-trial and error method could not longer meet the current demands for rapid reasearch and directional design of high-performance materials.Machine learning，as an important branch of artificial intelligence，provides effective guidance for material design by establishing a mapping relationship between input data and target performance，thereby shortening the cycle of material development.Based cutting-edge research results，the construction methods of machine learning in the materials science field are summarized.In response to issues such as small data volume，small dataset size and sample imbalance，etc，the methods for expanding datum in the materials science field mainly are extracting datum from publications，building data databases and conducting high-throughput experiments and caculations at the data source level.The main strategies for small data machine learning are active learning，transfer learning and Bayesian optimization.The opportunities and challenges of machine learning in the materials science field coexist. |
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