Complex concentrated alloys (CCA), with alternative names such as high-entropy alloys (HEA), compositional complex alloys (CCA), and multi-principal element alloys (MPEA), are an emerging group of metallic materials. The alloys’ name originates from their compositional complexity, where multiple principal elements are mixed in concentrated proportions. CCAs have attracted significant attention from the physical metallurgy community due to their potential to achieve a balanced combination of desirable properties for structural materials, such as high strength and hardness, excellent corrosion and fatigue resistance, acceptable ductility, and thermal stability. More excitingly, the multi-component feature grants HEAs a vast design space in terms of alloy composition. With only a tiny fraction of the space studied, this group of alloys is full of potential for groundbreaking discoveries. On the other hand, such a vast design space also disables the traditional alloy design approaches based on trial-and-error experiments. Novel design approaches that can effectively narrow down the promising regions of alloy compositions are highly desired.
At MCIG, we aim to navigate the design of high-performance CCAs through an integration of high-throughput computation, multiscale modeling, and data-science approaches. Through physics-informed machine learning (ML), we are engaged in developing surrogate models for efficient predictions of fundamental alloy properties across the entire chemical compositional space, from which promising candidates can be screened out for more accurate experimental investigations. Our recent research interests center on:
- Compositional-complexity-induced fluctuations in defect energetics and lattice distortion.
- Deformation mechanisms governing plastic strength and ductility.
- Phase stability prediction and microstructure control during processing.
Research Highlights