High-Throughput Computational Search for Novel Ternary Superalloys ID: 2015-063

This study uses high-throughput computational methods to identify novel ternary superalloys with superior mechanical properties.

Technology Overview

The research focuses on the computational discovery of new superalloys, primarily nickel, cobalt, and iron-based systems, using the quasi-random structure (SQS-32) approach within the AFLOW computational framework. It evaluates the thermodynamic stability and potential of 2224 ternary systems, identifying 2111 compound-forming systems and highlighting 102 systems as particularly stable.

Key Advantages

• Utilizes high-throughput computational techniques for efficient material discovery
• Employs a novel approach (SQS-32) to mimic random alloy statistics, enhancing the accuracy of predictions
• Identifies 2111 compound-forming ternary systems, significantly expanding the potential superalloy database
• Highlights 102 particularly stable systems, with 37 being novel, previously unreported systems

Problems Addressed

• Overcomes the limitations of conventional nickel-based superalloys
• Reduces the time and expense associated with experimental trial-and-error methods for material discovery
• Addresses the need for materials suitable for high-temperature applications

Additional Information

Technology ID: 2015-063
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Date Published: 28 March, 2025