Machine learning recommends affordable new Ti alloy with bone-like modulus

Publication date: Available online 27 September 2019Source: Materials TodayAuthor(s): Chun-Te Wu, Hsiao-Tzu Chang, Chien-Yu Wu, Shi-Wei Chen, Sih-Ying Huang, Mingxin Huang, Yeong-Tsuen Pan, Peta Bradbury, Joshua Chou, Hung-Wei YenAbstractA 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.Graphical abstract
Source: Materials Today - Category: Materials Science Source Type: research