Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning

Abstract

The optimal design of shape memory alloys (SMAs) with specific properties is crucial for the innovative application in advanced technologies. Herein, inspired by the recently proposed design concept of concentration modulation, we explore martensitic transformation (MT) in and design the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Systematic phase-field simulations generate data of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom. An ML-assisted strategy is adopted to perform multiobjective optimization of the mechanical properties, through which promising nanocomposite configurations are prescreened for the next set of phase-field simulations. The ML-guided simulations discover an optimized nanocomposite, composed of Nb-rich matrix and Nb-lean nanofillers, that exhibits a combination of mechanical properties, including ultralow modulus, linear super-elasticity, and near-hysteresis-free in a loading-unloading cycle. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work demonstrates the great potential of ML-guided phase-field simulations in the design of advanced materials with extraordinary properties.

Unravelling Role of the Microwave Sintering Effects on Microstructure, Density, and Corrosion Behavior of porous Ti-13.3at.% Nb Shape Memory Alloys

“…Recent advances in Ti-Nb-based SMAs have shown exceptional superelasticity, reaching up to 2.9-3% [11,12], satisfying the requirements for bone implantation. Despite the advantages mentioned earlier, it is essential to recognize that the Ti-Nb-based SMAs elastic modulus characteristic signi cantly surpasses the human bone elastic modulus (ranging from 6 to 26.6 GPa) [13]. This disparity in elastic moduli may cause a stress-shielding effect, which could contribute to implant failure.…”

Mechanical properties and behavior of the Ti–45Nb alloy subjected to extreme conditions

“…Using the theoretical approach, it is possible to investigate the influence of fine material characteristics tuning on their overall performance in the bio-environment in a quicker, more efficient, and more detailed manner. The research by Zhu et al 37 is a good example of how it is possible to accelerate the design of material microstructures that can ensure the appearance of desired mechanical properties. In their research, Zhu et al 37 used high-throughput phase-field simulations combined with machine learning techniques to obtain the Ti–Nb alloy nanocomposite with ultra-low elastic modulus, linear super-elasticity, and nearly free hysteresis, making it suitable for biomedical applications.…”

“…The research by Zhu et al 37 is a good example of how it is possible to accelerate the design of material microstructures that can ensure the appearance of desired mechanical properties. In their research, Zhu et al 37 used high-throughput phase-field simulations combined with machine learning techniques to obtain the Ti–Nb alloy nanocomposite with ultra-low elastic modulus, linear super-elasticity, and nearly free hysteresis, making it suitable for biomedical applications. However, the best and most reliable results can be achieved by combining experimental and theoretical techniques for further advancements in biometallic design.…”

Machine Learning-Assisted Synthesis of Two-Dimensional Materials

“…At present, machine learning (ML) plays a crucial role in various research fields and shows great potential and advantages in accelerating the development of materials. ML has been used to discover new materials, − such as catalytic materials, inorganic chalcogenides, − and thermoelectric materials, and to predict materials properties − including the stability of the perovskite structure, adsorption energy on metals, and lattice thermal conductivity of crystal materials . At the same time, ML also has many applications in CVD growth, such as predicting the material morphology. , The predictions of ML are routinely faster and cheaper than the traditional methods of computing from scratch.…”