Physics-informed machine learning prediction of the martensitic transformation temperature for the design of "NiTi-like" high entropy shape memory alloys

The present study proposes a physics-informed machine learning (PIML) algorithm-based approach aimed at predicting the martensitic transformation temperature (Ms) for the design of "NiTi-like" high entropy shape memory alloys (HESMAs). A previously established HESMAs database is enriched and extended to include bi-nary, ternary, quaternary, quinary and senary alloys containing the most employed alloying elements for HEAs design such as Ni equivalents (Fe, Cu, Co, Pd, Pt and Au), Ti equivalents (Zr and Hf), Nb and Ta. The Extremely Randomized Trees algorithm, based on the concept of multiple random decision tree predictions, is adopted as the regression method for Ms temperature prediction. Two strategies for the algorithm inputs have been adopted, discussed, and compared in terms of reliable predictions. The first relies on the composition of the alloying el-ements, whereas the second exploits a defined set of intrinsic material descriptors. The latter are based on mixing enthalpy, atomic radius, electronegativity, atomic number and number of elements. A high accuracy of the MS prediction has been reached when considering the material descriptors. In fact, the second strategy induces a mean absolute error that is less than 30 degrees C for alloys containing up to 4 elements. For more elements there are more discrepancies due to the homogenization state required for HEAs. The validation of the developed approach has been performed using 6 home-made HESMAs prepared specifically for this study. It demonstrated the pre-dictive capabilities of the developed physics-informed machine learning based approach. Finally, a HESMA design tool has been implemented to virtually design new HESMAs with a targeted Ms temperature above 400 degrees C. It is worth noting that this aspect is one of the most challenging engineering issues for such alloys. An illustrative case applied to the (NiCuPd)50(TiZr)50 family of alloys demonstrates the predictive capabilities of the developed approach to design such alloys to achieve a Ms temperature in the range of 300 degrees C to 700 degrees C.