Dataset and source code for: Physical metallurgy-informed regressor-chain learning for mechanical property prediction and completion in low-alloy steels

This deposit contains the dataset and source code supporting the findings of the manuscript [Physical metallurgy-informed regressor-chain learning for mechanical property prediction and completion in low-alloy steels] The foundational experimental records (chemical compositions, heat treatment parameters, and mechanical properties) were originally published in the supplementary of: N. Reddy, J. Krishnaiah, S. Hong, J. Lee, Modeling medium carbon steels by using artificial neural networks, Materials Science and Engineering a-Structural Materials Properties Microstructure and Processing, 508 (2009) 93–105. https://doi.org/10.1016/j.msea.2008.12.022. We acknowledge and credit the original authors for providing these valuable experimental data. Value Added: While the raw data originate from a single source, this deposit provides the processed and feature-augmented version specifically designed for machine learning. The raw appendix data lacked the necessary inputs for our models. Therefore, we performed calculation of physical-metallurgy-informed features(e.g., Ceq, Pcm, and so on)