Machine learning assisted empirical formula augmentation

• Published in: Materials & design Vol. 210; p. 110037
• Main Authors: Xiong, Bin, Zhao, Xinpeng, Hu, Yunfeng, Huang, Haiyou, Liu, Yang, Su, Yanjing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2021; Elsevier
• Subjects: Composition design; Empirical formula; Machine learning; Shape memory alloy; Transformation temperature; Shape memory alloy; Empirical formula; Composition design; Machine learning; Transformation temperature
• ISSN: 0264-1275; 1873-4197
• DOI: 10.1016/j.matdes.2021.110037

[Display omitted] •We have proposed an augmentation strategy for empirical formula based on machine learning mothed.•A novel empirical formula of Ms as the function of compositions was fitted based on an augmented dataset combined experimental data with predicted data.•Compared with previous empirical formula, the accuracy and robustness of the novel empirical formula we proposed was significantly improved without additional experimental cost.•This strategy offers a recipe to build empirical formula based on a small experimental dataset. Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key reference for composition design. Due to the lack of experimental data, a large deviation may exist among the prediction results from the empirical formulas obtained by different data sources. In present work, we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a Gaussian radial basis kernel function support vector machine (SVR.rbf) model was screened out based on mathematical and domain knowledge criteria. An augmented empirical formula of Ms as the function of compositions was fitted based on the abundant augmented dataset combined experimental data with predicted data by the SVR.rbf model. Compared with previous empirical formula fitted by small experimental dataset, the accuracy and robustness of the augmented empirical formula was significantly improved without additional experimental cost. This strategy offers a recipe to build empirical formula based on a small experimental dataset.