Study of intermetallics for corrosion and creep resistant microstructure in Mg-RE and Mg-Al-RE alloys through a data-centric high-throughput DFT framework

The design of corrosion-resistant alloys requires a thorough understanding of the chemistry, microstructure, and its evolution in atmospheric conditions. While the alloy processing conditions and chemistry determine the microstructure according to the phase diagram, the microstructure determines the behavior when exposed to corrosive environments. An automated data-centric high-throughput framework using density functional theory (DFT) based first-principles approach was developed to rank intermetallic chemistry and texture for their vulnerability in corrosive environments. As a test case, a user-interactive database for Mg-X-Y composition was created and made publicly available. The high-throughput framework was subsequently used to screen Mg-RE and Mg-Al-RE (RE = La, Ce, Nd) alloy precipitates from the database for corrosion resistance. The surface energies for different symmetrically distinct crystallographic orientations and work functions for the stable phases were calculated using an automated and intelligent workflow without requiring manual intervention. The predictions were compared against previous experimental and theoretical results, calculated properties and corrosion rates. The data was analyzed to provide guidance and insights on microstructure design with a focus on corrosion and creep resistant alloys. While the heat of formation seems to be a reliable material descriptor for the thermal stability of the phases for better creep resistance, the minimum surface energy is a reliable descriptor for the most stable crystallographic surfaces critical for corrosion resistance. These correlations established using a consistent first-principles based theory, which are computationally inexpensive, can simplify the exploration of possible elemental makeup and microstructural design and guide the design of corrosion and creep resistant alloys.