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About this Abstract
Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Thermodynamics and Kinetics of Alloys IV
Presentation Title A Data Science Approach to the Design of Compositionally Graded Alloys
Author(s) Mikayla Obrist, James Hanagan, Marshall Allen, Bernard Gaskey, Richard Malak, Saryu Fensin, Raymundo Arróyave
On-Site Speaker (Planned) Mikayla Obrist
Abstract Scope Conventional alloys often fall short in meeting the location-specific performance demands of advanced structural applications. Compositionally graded alloys (CGAs) offer a promising solution by enabling spatial tailoring of material properties within a single component. However, the vast number of possible compositional gradients across multi-element systems presents a major design challenge. To address this, a computational framework is developed to efficiently explore and classify large alloy property datasets. The framework encodes composition–structure–property relationships in a navigable data structure, enabling multi-objective screening, compositional clustering analysis, and flexible visualization comparisons of candidate alloys under a range of thermodynamic and mechanical property constraints. By applying these constraints, the framework segments the design space into interpretable regions feasible for CGAs that satisfy specific performance requirements. This approach is generalizable across alloy systems and is well-suited for integration with additive manufacturing workflows, where localized property optimization is critical.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation, High-Entropy Alloys

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