Datasets of narrow thermal hysteresis behaviour Ti-Ni-based HT-SMAs and the predicted accumulated local effects.

This article refers to data derived from a research article entitled "Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning" [1]. It is based on the knowledge that alloying Ti-Ni-based shape memory alloys (SMAs) with additional ternary or multicomponent elements can alter the SMAs' characteristic transformation temperatures, including the thermal hystereses. Two datasets are reported.

Machine learning-based prediction of phases in high-entropy alloys: A data article.

A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that research paper, titled "", is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures.