| DC Field | Value | Language |
|---|
| dc.contributor.author | Klimenko, D. | - |
| dc.contributor.author | Stepanov, N. | - |
| dc.contributor.author | Ryltsev, R. | - |
| dc.contributor.author | Yurchenko, N. | - |
| dc.contributor.author | Zherebtsov, S. | - |
| dc.date.accessioned | 2024-12-23T08:16:33Z | - |
| dc.date.available | 2024-12-23T08:16:33Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system / D. Klimenko, N. Stepanov, R. Ryltsev [et al.] // Intermetallics. - 2024. - Vol.175.-Art. 108469. - URL: https://www.sciencedirect.com/science/article/pii/S0966979524002887. | ru |
| dc.identifier.uri | http://dspace.bsu.edu.ru/handle/123456789/64204 | - |
| dc.description.abstract | The search for new high-entropy alloys (HEAs) with desired properties is an urgent problem that is hardly solvable experimentally due to the extremely large number of possible alloy compositions. Here we address developing data-driven machine learning models (DDML) to predict the ductility of HEAs | ru |
| dc.language.iso | en | ru |
| dc.subject | technique | ru |
| dc.subject | metal science | ru |
| dc.subject | high-entropy alloys | ru |
| dc.subject | machine learning | ru |
| dc.subject | data | ru |
| dc.subject | plasticity | ru |
| dc.subject | phenomenological models | ru |
| dc.subject | strength | ru |
| dc.title | Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system | ru |
| dc.type | Article | ru |
| Appears in Collections: | Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages)
|
