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Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning

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Abstract

Bulk metallic glasses (BMGs) have been receiving extensive attention in the community of physics and materials science due to their attractive properties. The traditional trial-and-error approach is inefficient in designing good BMGs, then it is imperative to elaborate a prediction scheme to accelerate the development of BGMs. This work combines a composite feature selection (c-FS) and machine learning (ML) algorithm to construct a more effective strategy for predicting the critical diameter (Dmax) of BMGs. Apart from the characteristic temperatures, parameters involving domain knowledge such as thermodynamics, dynamics, and topology are also considered as the candidate features. A c-FS was devised to optimize the features, and a most efficient model was developed using the optimal subset. Our model holds the highest R2 score (0.807) compared to the 29 criteria reported. In addition, the SHapley Additive exPlanation (SHAP) approach was introduced to boost the interpretability of the model. Benefiting from our model and SHAP approach, a formation rule for large-size BMGs is uncovered. The efficacy of this model provides new guidance for the design of BMGs.

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Data availability

All preprocessing and ML processes are performed on Python. The EFS, XGB, and BO algorithms are available in Mlxtend, XGBoost, and Optuna libraries, respectively. SHAP values are calculated by Shap library, and other algorithms are available in scikit-learn library. In addition, we have shared our data in Supplementary Materials.

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Acknowledgements

Authors acknowledge the financial support of the National Key R&D Program of China (Grant No. 2021YFB0300102), the National Natural Science Foundation of China (Grant Nos. 62376091, 51661005 and U1612442).

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Authors and Affiliations

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang, 550025, China

    Yangchuan Zeng, Zean Tian, Quan Zheng & Quan Xie

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Zean Tian & Anguo Bu

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YZ provided investigation, methodology, data curation, formal analysis, and writing—original draft. ZT performed investigation, supervision, writing—review and editing, and funding acquisition. QZ presented supervision, suggestion, and writing—review and editing. AB carried out supervision and suggestion. QX conducted supervision and suggestion.

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Zeng, Y., Tian, Z., Zheng, Q. et al. Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning. J Mater Sci 59, 8318–8337 (2024). https://doi.org/10.1007/s10853-024-09678-2

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