Element-configuration dependent first-principles machine learning studies of multiple alloying effects on the structure stability of Co3(Al, W)

Multiple alloying is desired to improve the stability of metastable Co3(Al, W) of Co-based superalloys. However, computational studies of alloying effects are costly due to the enormous combinations of compositions and configurations. Most current element-based machine learning methods that rely solely on composition lack the ability to distinguish between polymorph materials and various local alloying configurations. In this work, we developed efficient machine learning (ML) models using element-configuration dependent Center-Environment (CE) and element-based Chemical Composition (CC) features to describe alloying element type and substitution configuration, crucial for data-driven study of correlated alloying effects and local short-range chemical ordering. We employed first-principles computation and ML methods to investigate the structure stabilities of the Al-W substitution site models with 3d and 4d transition metal (TM) elements, then the optimized ML models were validated using the untrained dual-Al or dual-W site configurations with 3d, 4d, and 5d TM elements. The ML-CE models were generally more accurate than the ML-CC models. The most stable substitution pairs are Sc-Fe 3d TM elements at the Al-W and dual-W sites with lattice constant-4 A. Therefore, we propose a "Co-left" design rule to stabilize Co3(Al, W) by multiple alloying with early 3d TM elements (IIIB-VIII group). The stabilizing 3d "Co-left" elements also can improve the mechanical properties. The ML-CE approach can be generally applied to study local correlated alloying effects and chemical ordering in medium/high-entropy or amorphous alloys.