Semantic Scholar Open Access 2023 596 sitasi

Small data machine learning in materials science

Pengcheng Xu Xiaobo Ji Minjie Li Wencong Lu

Lihat Sumber DOI

Abstrak

This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.

Penulis (4)

Pengcheng Xu

Xiaobo Ji

Minjie Li

Wencong Lu

Format Sitasi

APA MLA BibTeX

Xu, P., Ji, X., Li, M., Lu, W. (2023). Small data machine learning in materials science. https://doi.org/10.1038/s41524-023-01000-z

Akses Cepat

Lihat di Sumber doi.org/10.1038/s41524-023-01000-z

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 596×
Sumber Database: Semantic Scholar
DOI: 10.1038/s41524-023-01000-z
Akses: Open Access ✓