Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as potential
replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their
high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space
using exclusively experimental approaches is impractical due to the considerable cost of the synthesis,
processing, and testing of candidate alloys, particularly at operation-relevant temperatures. On the other hand,
the lack of reasonably accurate predictive property models, especially for high-temperature properties, makes
traditional Integrated Computational Materials Engineering (ICME) methods inadequate. In this paper, we
address this challenge by combining machine-learning models, easy-to-implement physics-based models, and
inexpensive proxy experimental data to develop robust and fast-acting models using the concept of Bayesian
updating. The framework combines data from one of the most comprehensive databases on RHEAs (Borg et al.,
2020) with one of the most widely used physics-based strength models for BCC-based RHEAs (Maresca and
Curtin, 2020) into a compact predictive model that is significantly more accurate than the state-of-the-art.
This model is cross-validated, tested for physics-informed extrapolation, and rigorously benchmarked against
standard Gaussian process regressors (GPRs) in a toy Bayesian optimization problem. Such a model can be
used as a tool within ICME frameworks to screen for RHEAs with superior high-temperature properties. The
code associated with this work is available at: https://codeocean.com/capsule/7849853/tree/v2.
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Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys
Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a reason for many of those alloys’ properties. Nevertheless, accurate characterizations of lattice distortion are still scarce to only cover a tiny fraction of HEA’s giant composition space due to the expensive experimental or computational costs. Here we present a physics-informed statistical model to efficiently produce high-throughput lattice distortion predictions for refractory non-dilute/high-entropy alloys (RHEAs) in a 10-element composition space. The model offers improved accuracy over conventional methods for fast estimates of lattice distortion by making predictions based on physical properties of interatomic bonding rather than atomic size mismatch of pure elements. The modeling of lattice distortion also implements a predictive model for yield strengths of RHEAs validated by various sets of experimental data. Combining our previous model on intrinsic ductility, a data mining design framework is demonstrated for efficient exploration of strong and ductile single-phase RHEAs.
more » « less- NSF-PAR ID:
- 10405381
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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