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Updated: Jun 23, 2026

Applying Dynamic Strain on Thin Oxide Films Immobilized on a Pseudoelastic Nickel-Titanium Alloy

Applying Dynamic Strain on Thin Oxide Films Immobilized on a Pseudoelastic Nickel-Titanium Alloy

Published on: July 28, 2020

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

Camilo A F Salvador¹, Bruno F Zornio¹, Caetano R Miranda¹

¹Instituto de Física, DFMT, Universidade de São Paulo, CP 66318, 05315-970 São Paulo, SP, Brazil.

ACS Applied Materials & Interfaces

|December 9, 2020

Summary

This summary is machine-generated.

Machine learning models predict elastic properties of titanium alloys for biomedical use. Optimized Ti-Nb-Zr alloys with 22% Zr show promise for low-modulus, stable materials.

Keywords:

elasticity first-principles calculations machine learning metals and alloys phase transitions

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Area of Science:

Materials Science
Computational Materials Science
Biomedical Engineering

Background:

Discovering low-modulus titanium (Ti) alloys for biomedical applications is complex due to numerous compositions and solute contents.
Predicting elastic properties like bulk modulus (K), shear modulus (G), and Young's modulus (E) is crucial for material design.

Purpose of the Study:

To employ machine learning (ML) methods for predicting the elastic moduli (K and G) of optimized ternary alloys.
To identify promising Ti-Nb-Zr alloy compositions with low elastic modulus and high beta-phase stability for biomedical applications.

Main Methods:

Utilized elasticity data from over 1800 compounds to train linear models, random forest regressors (RF), and artificial neural networks (NN).
Applied ML models to predict Young's modulus (E) across all compositions in the Ti-Nb-Zr system with 2 at.% variations.
Investigated optimal compositions using special quasi-random structures (SQSs) and density functional theory (DFT).

Main Results:

RF and NN models showed good agreement in predicting Young's modulus (E), with deviations less than 4 GPa.
Identified alloys with 22% Zr as promising for biomedical applications due to low elastic modulus and stable beta phase.
Found that Nb content above 14.8 at.% favors beta phase over the high-modulus omega phase, potentially preventing its formation.

Conclusions:

Machine learning effectively predicts elastic properties of Ti alloys, aiding in the discovery of new materials.
Ti-Nb-Zr alloys with specific compositions (e.g., 22% Zr) offer a favorable combination of low elastic modulus and phase stability for biomedical implants.
The study provides a pathway to design advanced titanium alloys by mitigating the formation of undesirable high-modulus phases.