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 modul...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 superelasticity,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.展开更多
基金The work is supported by the National Key R&D Program of China(No.2018YFB0704404)the National Natural Science Foundation of China(Grant Nos.11802169 and 12172370).
文摘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 superelasticity,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.
