Abstract: 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.

Machine learning recommends affordable new Ti alloy with bone-like modulus
Read full text on ScienceDirect

DOI: 10.1016/j.mattod.2019.08.008