About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Elements Alloys X: Structures and Modeling
|
| Presentation Title |
Expediting the Materials Discovery Process of MPEAs through Efficient Coupling of High-throughput Density Functional Theory, Molecular Dynamics and Machine Learning Techniques |
| Author(s) |
Jacob Startt, Mitchell Wood, Sean Donegan, Remi Dingreville |
| On-Site Speaker (Planned) |
Jacob Startt |
| Abstract Scope |
Refractory high entropy alloys (RHEA), multi-principal element alloys (RMPEA), or concentrated, complex alloys (RCCA) are a promising new class of material offering excellent mechanical strength coupled with high-temperature stability. These alloys present a large compositional space, however, making discovery and optimization difficult. Machine learning (ML) techniques offer a path for efficiently navigating this complex space. In this work we employ two levels of ML to (i) build a highly-accurate computational dataset for the MoNbTa-class alloy and (ii) to rapidly explore the compositional landscape for optimized sets of desired material properties. We first employ high-fidelity density functional theory (DFT) simulations to train a SNAP (spectral neighbor analysis potential) interatomic molecular dynamics (MD) potential. This SNAP potential is then used to generate a large thermodynamic and mechanical property database through which ML algorithms search for optimal alloy compositions. This work demonstrates a viable path towards expediting the materials discovery process for MPEAs. |
| Proceedings Inclusion? |
Planned: TMS Journal: Metallurgical and Materials Transactions |
| Keywords |
Machine Learning, Modeling and Simulation, High-Entropy Alloys |