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. The first and primary dataset documents experimental Ti-Ni-based shape memory alloys' high-transformation temperature characteristics reported in the literature. The second auxiliary dataset presented in this article was obtained following the explainable prediction of the narrow high-temperature thermal hysteresis behaviour in Ti-Ni-based high-transformation temperature SMAs (HT-SMAs). The second dataset is intended to generalise and summarise the ML prediction and visualisation of the thermal hysteresis behaviour as also observed experimentally in multiple reports elsewhere. The datasets are provided as supplementary files and the second dataset is also visualised as an intuitive marginal effects plot. We believe that these data will find applications in advancing experimental and theoretical HT-SMA research.

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 "Machine learning-based prediction of phases in high-entropy alloys", is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields.