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Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects
The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to unc...
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| Pubblicato in: | Materials (Basel) |
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| Autori principali: | , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6981756/ https://ncbi.nlm.nih.gov/pubmed/31906401 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/ma13010179 |
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