|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Expediting the materials discovery process of MPEAs through efficient coupling of high-throughput density functional theory, molecular dynamics and machine learning techniques
||Jacob Startt, Mitchell Wood, Sean Donegan, Remi Dingreville
|On-Site Speaker (Planned)
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.
||Machine Learning, Modeling and Simulation, High-Entropy Alloys