Effects of Changing Atomic Concentration of a Single Element on the Yield Strength of High-Entropy Alloys: A Causal Inference Study

High-entropy alloys (HEAs) are renowned for their exceptional mechanical properties, including superior yield strength, making them ideal for various demanding applications. This article explores the intricate relationship between the atomic concentration of single elements within HEAs and their yield strength, utilizing causal inference to uncover cause-and-effect relationships. By employing machine learning techniques, the study estimates the magnitude of changes in yield strength when specific HEA variables, such as valence electron concentration, electronegativity difference, atomic size difference, mixing enthalpy, and mixing entropy, are altered. The research highlights the significant impact of these variables on the mechanical properties of HEAs, offering valuable insights for materials design and optimization. The findings provide a deeper understanding of how individual elements and their concentrations influence the yield strength of HEAs, paving the way for more targeted and efficient material development. The study also compares different machine learning methods, demonstrating the robustness and reliability of the proposed approach. This comprehensive analysis not only advances the field of materials science but also sets a new standard for integrating causal inference and machine learning in materials research.