A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys.

The present work depicts a compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys and their respective microstructural features. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation.

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations.

The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus () and the shear modulus () of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for and based on compositional features.

Solute lean Ti-Nb-Fe alloys: An exploratory study.

In this study, we explored the Ti-Nb-Fe system to find an optimal cost-effective composition with the lowest elastic modulus and the lowest added Nb content. Six Ti-(31-4x)Nb-(1+0.5x)Fe ingots were prepared and Nb was substituted with Fe, starting at Ti-31Nb-1.