Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Abstract

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

“…BO has been deployed for the optimization and discovery of many different materials 12,23–26 in the lab or a computer simulation, including nanoporous materials, 27–31 nanoparticles, 32 light emitting diodes, 33 carbon nanotubes, 34 photovoltaics, 35–37 additively manufactured structures, 38 polymers, 39–43 thermoelectrics, 44 anti-microbial active surfaces, 45 quantum dots, 46 luminescent materials, 47 catalysts, 48–52 thin films, 53 solid chemical propellants, 54 alloys, 55 and phase-change memory materials. 56 More, BO has been used to optimize processes to synthesize materials and chemicals 57–63 or to employ materials for an industrial-scale task.…”

Predicting Mechanical Properties of Non-Equimolar High-Entropy Carbides using Machine Learning

“…adopting the composition-based feature vector (CBFV) as substitution. Composition-based feature vector (CBFV) is the descriptive statistics (mean, range, sum and variance) of the composition elements which has been successfully applied in materials research [26][27][28] . For the RF model, we revised the previous complicated input features, adopting a series of CBFV [28][29][30][31] -Jarvis, Magpie, Mat2vec, Onehot, Oliynyk, and random -elemental descriptors as substitution to eliminate the impact of structural information, achieving the prediction of mechanical properties only from the perspective of composition.…”