About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Computing interdiffusion coefficients in multi-principal element alloys from first principles |
| Author(s) |
Shashank Saxena, Damien K. J. Lee, Anirudh Raju Natarajan |
| On-Site Speaker (Planned) |
Shashank Saxena |
| Abstract Scope |
Quantifying mass transport in multi-principal-element alloys (MPEAs) from first principles remains a formidable challenge owing to their chemical diversity. Identifying diffusion mechanisms and the modes of intermixing in these alloys is crucial to designing materials that are processable at reasonable temperatures. Here, we target MPEAs containing elements from groups 4, 5, and 6 of the periodic table. Using high-throughput electronic structure calculations, embedded cluster expansions, machine learning, and finite-temperature simulations, we characterize diffusion in multicomponent refractory alloys. We show how on-lattice embedded local cluster expansion (eLCE) models rigorously estimate non-dilute, finite-temperature Onsager transport coefficients. Combined with the thermodynamic factor obtained from Monte Carlo simulations, this approach yields the diffusion tensor for industrially relevant MPEAs, from which we compute interdiffusion modes and diffusivities. We further discern how varying elemental concentrations affect measurable properties such as intermixing coefficients and Kirkendall effect. Together, these methods offer a guide toward finding processable alloy compositions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |