Towards Stacking Fault Energy Engineering in FCC High Entropy Alloys
arXiv:2111.03591 · doi:10.1016/j.actamat.2021.117472
Abstract
Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many years as a key intrinsic mechanical property, its inference via experimental observations or prediction using, for example, computationally intensive first-principles methods is challenging. This difficulty precludes the explicit use of SFE as an alloy design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict the SFE in the fcc CoCrFeMnNiV-Al high-entropy alloy space. The best-performing ML model is capable of accurately predicting the SFE of arbitrary compositions within this 7-element system. This efficient model along with a recently developed model to estimate intrinsic strength of fcc HEAs is used to explore the strength-SFE Pareto front, predicting new-candidate alloys with particularly interesting mechanical behavior.
arXiv comment27 pages, 16 figures, 1 Table
References in corpus (16)
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
- A Review of Feature Selection Methods Based on Mutual Information
- Tunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys
- SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
- Libxc: a library of exchange and correlation functionals for density functional theory
- Application of Finite Element, Phase-field, and CALPHAD-based Methods to Additive Manufacturing of Ni-based Superalloys
- Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy
- Accelerating computational modeling and design of high-entropy alloys
- Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging
- Ta-Nb-Mo-W refractory high-entropy alloys: anomalous ordering behavior and its intriguing electronic origin
- Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning
- Vacancy-mediated complex phase selection in high entropy alloys
- Structural Properties and Relative Stability of (Meta)Stable Ordered, Partially-ordered and Disordered Al-Li Alloy Phases
- On the martensitic transformation in FeMnCoCr high-entropy alloy
- Chemically-Mediated quantum criticality in NbFe_2
- Designing order-disorder transformation in high-entropy ferritic steels