Speaker
Description
Shape-memory alloys are unique materials capable of undergoing large reversible strains and exhibiting the shape-memory effect, which is driven by external changes of temperature. These remarkable properties are based on a martensitic transformation between austenite (high-temperature phase) and martensite (low-temperature phase). The NiTi shape memory alloy has become the most widely used shape memory material in industrial, high-tech, and medical applications due to its unique thermal and mechanical properties.
In this work we aim at fundamental understanding of the behavior of twins in the martensite structure during mechanical loading. In order to consider a sufficient number of internal degrees of freedom, we constructed relatively large supercells representing the perfect and twinned martensite and studied their responses to shear and tensile loading. Such calculations are computationally very demanding when using ab initio approaches. Therefore, we developed a novel machine-learnt (ML) interatomic potential, tailored specifically for the martensite structure. Computationally accessible predictions based on the ML potential were benchmarked from first principles.
