A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses
| Autor: | Yanjun Zhang, G.C. Xing, Leong Hien Poh, Zhen-Dong Sha |
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| Rok vydání: | 2021 |
| Předmět: |
Fusion
Amorphous metal Structural material Training set Materials science business.industry Mechanical Engineering Two step Metals and Alloys 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Glass forming 0104 chemical sciences Mechanics of Materials Casting (metalworking) Materials Chemistry Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Journal of Alloys and Compounds. 875:160040 |
| ISSN: | 0925-8388 |
| Popis: | Metallic glasses (MGs) are often perceived as quintessential structural materials. However, the widespread application of MGs is hindered primarily by their limited glass-forming ability (GFA) for the manufacture of large-scale MGs. In this work, a two-step fused machine learning (ML) approach is proposed, aiming to provide an efficient tactic for the precise prediction of MGs with robust GFA. In our ML framework, alloy compositions are the only required inputs. Moreover, the dataset comprises alloys that can and cannot be cast into MGs. This departs from the conventional ML approach utilizing only a correct set of training data (i.e. alloys that can cast into MGs). The fusion algorithm is also employed to further improve the performance of ML approach. The critical casting sizes predicted by our ML model are in good agreement with those reported in experiments. This work has extensive implications for the design of bulk MGs with superior GFA. |
| Databáze: | OpenAIRE |
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