Machine learning–enabled high-entropy alloy discovery

Abstract

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10 −6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.

Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength

“…In regards to other properties, Wen et al developed an ML-based model to predict hardness in the Al x Co y Cr z Cu u Fe v Ni w system, which was coupled with an experimental synthesis and optimization approach 34 . ML-based studies on the catalytic and thermal expansion properties of HEAs have also performed 33,35,36 . An ML-based framework informed by extant literature data is still a mostly untapped route for the development of HEAs with superior mechanical properties, particularly for RHEAs which possess the potential for high strength in high-temperature applications.…”

Discovering the Ultralow Thermal Conductive High-Entropy Pyrochlore Oxides Through the Hybrid Knowledge-Assisted Data-Driven Machine Learning

“…The appealing functionality can be originated from their superior compositional exibility, which can be described as large lattice distortion, sluggish kinetics, increased size and mass disorder [4][5][6][7] . These descriptive parameters could be treated as the genes/basic building-blocks or the so-called key physical parameters (KPPs) addressing the correlations between mechanical and thermal properties, which present a relatively straightforward physics-informed relationship with the target property/performance 4,8,9 , such as lattice distortion for solid-solution strengthen and alloy composition for thermal expansion coe cient and heat capacity 9 . Since the A 2 B 2 O 7 type high-entropy pyrochlore oxides (HEPOs) have emerged as leading candidates for a new generation of TBC with signi cantly lower thermal conductivity and chemical stability at higher temperatures 10 .…”

“…Meanwhile, Debye temperature can be affected dramatically by the Grüneisen parameters, which can be obtained/predicted by various de nitions from both macroscopic and microscopic levels 28 . Moreover, the data-driven and data-intensive machine learning (ML) algorithms have become promising viable strategies, which offer novel solutions to dig out the hidden connection among existing databases and predict desired material target properties 8,9,29,30 . Recently, it has been emphasized on the importance of direct descriptor-target prediction 8 and multifaceted modeling approach 9 for materials design, such as applying the integrated ML, computational thermodynamics and lattice engineering to examine the shape-memory behavior of zirconia ceramics 4 .…”

“…Moreover, the data-driven and data-intensive machine learning (ML) algorithms have become promising viable strategies, which offer novel solutions to dig out the hidden connection among existing databases and predict desired material target properties 8,9,29,30 . Recently, it has been emphasized on the importance of direct descriptor-target prediction 8 and multifaceted modeling approach 9 for materials design, such as applying the integrated ML, computational thermodynamics and lattice engineering to examine the shape-memory behavior of zirconia ceramics 4 . The investigations of modeling κ by ML methods have concentrated on establishing composition-property regression models 31,32 and ML interatomic potentials accelerated approach 33,34 .…”

Machine learning-enabled design of architected materials

“…Unlike the Bayesian methods, the autoencoder learns an effective representation of the high-dimensional data in an unsupervised manner, which converts the exploration in a highdimensional design space into a lower one. This method has been proven to be a revolutionary technique in materials discovery (27,28). However, to the best of our knowledge, this is the first time that a 3D convolutional autoencoder(3D-CAE) has been applied to 3D structure generation with high dimensionality (for details see Section S2).…”