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Abstract The discovery of complex concentrated alloys (CCA) has unveiled materials with diverse atomic environments, prompting the exploration of solute segregation beyond dilute alloys. However, the vast number of possible elemental interactions means a computationally prohibitive number of simulations are needed for comprehensive segregation energy spectrum analysis. Data-driven methods offer promising solutions for overcoming such limitations for modeling segregation in such chemically complex environments (CCEs), and are employed in this study to understand segregation behavior of a refractory CCA, NbMoTaW. A flexible methodology is developed that uses composable computational modules, with different arrangements of these modules employed to obtain site availabilities at absolute zero and the corresponding density of states beyond the dilute limit, resulting in an extremely large dataset containing 10 million data points. The artificial neural network developed here can rely solely on descriptions of local atomic environments to predict behavior at the dilute limit with very small errors, while the addition of negative segregation instance classification allows any solute concentration from zero up to the equiatomic concentration for ternary or quaternary alloys to be modeled at room temperature. The machine learning model thus achieves a significant speed advantage over traditional atomistic simulations, being four orders of magnitude faster, while only experiencing a minimal reduction in accuracy. This efficiency presents a powerful tool for rapid microstructural and interfacial design in unseen domains. Scientifically, our approach reveals a transition in the segregation behavior of Mo from unfavorable in simple systems to favorable in complex environments. Additionally, increasing solute concentration was observed to cause anti-segregation sites to begin to fill, challenging conventional understanding and highlighting the complexity of segregation dynamics in CCEs. 

Abstract Entropy‐stabilized oxide (ESO) research has primarily focused on discovering unprecedented structures, chemistries, and properties in the single‐phase state. However, few studies discuss the impacts of entropy stabilization and secondary phases on functionality and in particular, electrical conductivity. To address this gap, electrical transport mechanisms in the canonical ESO rocksalt (Co,Cu,Mg,Ni,Zn)O are assessed as a function of secondary phase content. When single‐phase, the oxide conducts electrons via Cu⁺/Cu²⁺small polarons. After 2 h of heat treatment, Cu‐rich tenorite secondary phases form at some grain boundaries (GBs), enhancing grain interior electronic conductivity by tuning defect chemistry toward higher Cu⁺carrier concentrations. 24 h of heat treatment yields Cu‐rich tenorite at all GBs, followed by the formation of anisotropic Cu‐rich tenorite and equiaxed Co‐rich spinel secondary phases in grains, further enhancing grain interior electronic conductivity but slowing electronic transport across the tenorite‐rich GBs. Across all samples, the total electrical conductivity increases (and decreases reversibly) by four orders of magnitude with heat‐treatment‐induced phase transformation by tuning the grains’ defect chemistry toward higher carrier concentration and lower migration activation energy. This work demonstrates the potential to selectively grow secondary phases in ESO grains and at GBs, thereby tuning the electrical properties using microstructure design, nanoscale engineering, and heat treatment, paving the way to develop many novel materials.