| DC Field | Value | Language |
|---|
| dc.contributor.author | Klimenko, D. | - |
| dc.contributor.author | Stepanov, N. | - |
| dc.contributor.author | Jia Li | - |
| dc.contributor.author | Qihong Fang | - |
| dc.contributor.author | Zherebtsov, S. V. | - |
| dc.date.accessioned | 2024-04-24T07:13:01Z | - |
| dc.date.available | 2024-04-24T07:13:01Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | Machine learning-based strength prediction for refractory high-entropy alloys of the Al-Cr-Nb-Ti-V-Zr system / D. Klimenko, N. Stepanov, Jia Li [et al.] // Materials. - 2021. - Vol.14, №3.-Art. 7213. | ru |
| dc.identifier.uri | http://dspace.bsu.edu.ru/handle/123456789/62461 | - |
| dc.description.abstract | The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling | ru |
| dc.language.iso | en | ru |
| dc.subject | technique | ru |
| dc.subject | metal science | ru |
| dc.subject | alloys | ru |
| dc.subject | high entropy alloys | ru |
| dc.subject | machine learning | ru |
| dc.subject | prediction | ru |
| dc.subject | strength | ru |
| dc.subject | structure | ru |
| dc.title | Machine learning-based strength prediction for refractory high-entropy alloys of the 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)
|
