|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Computational Thermodynamics and Kinetics
||Intrinsic Point Defect in Intermetallics: From Computation to Data Mining
||Wei Chen, Hong Ding, Bharat Medasani, Maciej Haranczyk, Kristin Persson, Mark Asta
|On-Site Speaker (Planned)
Point defects play an important role in determining the structural stability and mechanical behavior of intermetallic compounds. To help quantitatively understand point defects in these compounds, we developed PyDII, a Python framework that performs thermodynamic calculations of equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallics. The algorithm is based on a dilute-solution thermodynamic formalism with defect excitation energies calculated from first-principles. The analysis module in PyDII enables automated calculations of intrinsic antisite and vacancy concentrations as a function of composition and temperature, and the point defect concentration changes arising from addition of an extrinsic substitutional solute species. Using PyDII, we have computed point defect properties of 100 B2 intermetallics within Materials Project high-throughput infrastructure. Tree-based classification models were trained with the data set to predict preferred defect type in intermetallics. Our models predict promising alloy systems that demonstrate desirable defect preferences for high-temperature structural alloy application.
||Planned: A print-only volume