| Autor: |
Wu, CT, Chang, HT, Wu, CY, Chen, SW, Huang, SY, Huang, M, Pan, YT, Bradbury, P, Chou, J, Yen, HW |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Popis: |
© 2019 Elsevier Ltd A neural-network machine called “βLow” enables a high-throughput recommendation for new β titanium alloys with Young's moduli lower than 50 GPa. The machine was trained by using a very general approach with small data from experiments. Its efficiency and accuracy break the barrier for alloy discovery. βLow's best recommendation, Ti-12Nb-12Zr-12Sn (in wt.%) alloy, was unexpected in previous methods. This new alloy meets the requirements for bio-compatibility, low modulus, and low cost, and holds promise for orthopedic and prosthetic implants. Moreover, βLow's prediction guides us to realize that the unexplored space of the chemical compositions of low-modulus biomedical titanium alloys is still large. Machine-learning-aided materials design accelerates the progress of materials development and reduces research costs in this work. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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