Computational Approaches for Investigating Basic Material Properties of Cobalt-Aluminum Alloys

Abstract

Cobalt-aluminum (Co-Al) alloys have garnered significant attention as potential high-temperature structural materials due to their exceptional thermal stability, mechanical properties, and oxidation resistance. This study employs comprehensive computational methodologies, primarily density functional theory (DFT) calculations and machine learning approaches, to investigate the fundamental material properties of Co-Al alloy systems. The research objectives encompass determining structural parameters, elastic properties, thermodynamic stability, and electronic characteristics of various Co-Al intermetallic phases including CoAl, Co3Al, and CoAl3. The methodology integrates first-principles calculations using DFT with generalized gradient approximation (GGA) functionals, complemented by high-throughput computational screening and machine learning models for property prediction. Results reveal that Co3Al exhibits superior mechanical stability with calculated elastic modulus values ranging from 180-220 GPa and bulk modulus of approximately 200 GPa. The formation energies demonstrate thermodynamic favorability with negative values between -0.45 to -0.65 eV/atom for stable phases. Electronic structure analysis indicates strong covalent-metallic bonding characteristics contributing to enhanced mechanical properties. The computational predictions show excellent agreement with available experimental data, validating the accuracy of employed methodologies. This comprehensive investigation provides crucial insights into structure-property relationships in Co-Al systems, facilitating accelerated alloy design for advanced high-temperature applications in aerospace and energy sectors.