| Autor: |
Binglin Xie, Xianhua Yao, Weining Mao, Mohammad Rafiei, Nan Hu |
| Rok vydání: |
2022 |
| DOI: |
10.21203/rs.3.rs-1241474/v1 |
| Popis: |
Modern AI-assisted approaches have helped material scientists revolutionize their abilities to better understand the properties of materials. However, current machine learning (ML) models would perform awful for materials with a lengthy production window and a complex testing procedure because only a limited amount of data can be produced to feed the model. Here, we introduce self-supervised learning (SSL) to address the issue of lacking labeled data in material characterization. We propose a generalized SSL-based framework with domain knowledge and demonstrate its robustness to predict the properties of a candidate material with the fewest data. Our numerical results show that the performance of the proposed SSL model can match the commonly-used supervised learning (SL) model with only 5 % of data, and the SSL model is also proven with ease of implementation. Our study paves the way to expand further the usability of ML tools for a broader material science community. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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