Speaker
Description
Machine learning based potentials represent a new powerful tool for accurate modelling of complex interatomic interactions in materials. In our work, we apply the high-dimensional neural network (HDNNP) methodology using the atom-centred symmetry function descriptors for the shape memory alloy NiTi in the martensitic phase.
NiTi is a system that poses significant challenges for both experimental and theoretical modelling, and an accurate atomistic scale model is required to fully understand all of it's mechanical properties. Recent experimental results from thermomechanical testing and microstructural analysis of polycrystalline NiTi shape memory alloys have provided a qualitatively new picture of the plastic deformation mechanisms of this most common shape memory material. In particular, they challenge the widely accepted paradigm that austenite is a phase more susceptible to dislocation slip and plastic deformation in NiTi than B19' martensite, despite the fact that only a single 100M dislocation slip system is observed in martensite. The simultaneous activation of 100M plastic slip and reorientation of martensite between two variants sharing the (010)M plane results in kink banding and twinning, forming the so-called "kwink bands.
We show that our newly developed interatomic is suitable for simulating the evolution of the martensitic microstructure, is close in accuracy to density functional theory, preserves the strong plastic slip anisotropy and has all the prerequisites to simulate the new kwink mechanism.
