Accelerated design of linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field simulations and machine learning
Yuquan Zhu, Tao Xu, Qinghua Wei, Hongxin Yang, Takahiro Shimada,, Takayuki Kitamura, Tong-Yi Zhang

TL;DR
This paper introduces a computational approach combining phase-field simulations and machine learning to design Ti-Nb nanocomposites with ultralow modulus and superelasticity, advancing the development of functional shape memory alloys.
Contribution
It develops a high-throughput simulation and ML-guided optimization framework for designing nanocomposites with tailored mechanical properties, including ultra-low modulus and superelasticity.
Findings
Designed nanocomposites with ultra-low modulus and superelasticity
ML-assisted multi-objective optimization accelerates material discovery
Optimized microstructures exhibit continuous martensitic transformation
Abstract
The controlled design of martensitic transformation (MT) to achieve specific properties is crucial for the innovative application of shape memory alloys (SMAs) in advanced technologies. Herein, we explore and design the MT behaviors and the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Based on the systematic phase-field simulations, we obtain data sets of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom, which can be employed to design and optimize the microstructures for different applications. To accelerate the phase-field screening of the desired metallic biomaterials, a ML assisted strategy is adopted to perform multi-objective optimization of the mechanical properties, through which promising nanocomposite configurations are pre-screened…
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Taxonomy
TopicsShape Memory Alloy Transformations · Titanium Alloys Microstructure and Properties · High Entropy Alloys Studies
