Abstract
Doping point defects into shape memory alloys (SMAs) influences their transformation and mechanical properties. We propose a general Landau free energy model to study doping effects, which only assume that point defects produce local dilatational stresses coupled to non-order parameter volumetric strain. Different dopants can be represented by their range of interaction and potency of dilatational stress. Time-dependent simulations based on our model successfully reproduce experimentally observed doping effects on SMAs, including the elevation or suppression of the transformation temperature, the modification of mechanical properties, the appearance of a cross-hatched tweed structure and the emergence of a frozen glassy state with local strain order. We further predict that the temperature range for superelasticity will be optimized in a certain composition range between martensite and strain glass states. And an Elinvar effect appears most likely in alloys with dopants tending to increase the transformation temperature, which needs to be verified experimentally. Moreover, the two dopant parameters in the Landau model, the interaction range and potency of the dilatational stress, inspire us to identify three material descriptors with which we can construct a surrogate or empirical model to predict the behavior of the transformation temperature, as well as the slope of the change in transformation temperature versus composition, enabling an effective search for doped SMAs with targeted properties via machine learning.
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