Design of Fe-based bulk metallic glasses for maximum amorphous diameter (Dmax) using machine learning models
| Autor: | J. Moya, Daniel G. Mastropietro |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Work (thermodynamics)
Boosting (machine learning) Materials science General Computer Science Alloy General Physics and Astronomy 02 engineering and technology engineering.material 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Set (abstract data type) General Materials Science Amorphous metal business.industry General Chemistry 021001 nanoscience & nanotechnology 0104 chemical sciences Amorphous solid Computational Mathematics Tree (data structure) Mechanics of Materials Learning curve engineering Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Computational Materials Science. 188:110230 |
| ISSN: | 0927-0256 |
| Popis: | The development of bulk metallic glasses (BMGs) is a topic of current interest due to the unique set of properties that distinguish them from their crystalline counterpart and make them attractive in industrial applications as both structural and functional materials. Currently, a great effort is being made to model and quantify the glass forming ability of the amorphous in an alloy, as well as in tuning their properties in view of the final application of the material. In this work we have used two machine learning techniques, multiple linear regression and tree boosting, to predict the maximum amorphous diameter of Fe-based BMGs, exclusively from the alloy’s chemical composition. The modeĺs predictive power is characterised by a predicted-R2 of 0.71 (predicted-R = 0.84) and a training-R2 of 0.90 (training-R = 0.95) over a set of 480 alloys present in the dataset. Learning curves are employed as part of a comparative prediction analysis of the two techniques and to help decide the modelling aspects on which effort should be invested in the future. Selected examples using pseudo-ternary diagrams for the design of new Fe-based BMGs are presented, where the potential of the model becomes clear. |
| Databáze: | OpenAIRE |
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