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Refractory high-entropy alloys (RHEAs) are a promising class of alloys that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. However, exploring the vast RHEA…
Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a…
Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers…
Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use…
Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of…
Refractory high-entropy alloys (RHEAs) are compositionally complex materials which have been demonstrated to have the potential for exceptional strength at high operating temperatures. However, their composition space is vast, and other…
Traditionally, yield strength prediction relies on detailed and resource-intensive microstructural characterization combined with empirical equations. However, quantifying microstructural feature length scales for novel processes like…
The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify…
The design of high-entropy alloys (HEA) with desired properties is challenging due to their large compositional space. While various machine learning (ML) models can predict specific HEA solid-solution phases (SS), predicting high-entropy…
High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response.…
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex…
The tunability of the mechanical properties of refractory multi-principal-element alloys (RMPEAs) make them attractive for numerous high-temperature applications. It is well-established that the phase stability of RMPEAs control their…
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a…
This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and…
Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a…
Corrosion poses a significant challenge to the performance of aluminum alloys, particularly in marine environments. This study investigates the application of machine learning (ML) algorithms to predict and optimize corrosion resistance,…
Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy…
Additively manufactured (AM) aluminum alloys with high strength and thermal stability have broad applications in turbine engines, vacuum pumps, heat exchangers, and many other industrial systems. Employing precipitates with an L1$_2$…
The growing need for structural materials with strength, mechanical stability, and durability in extreme environments is driving the development of high entropy alloys. These are materials with near equiatomic mixing of five or more…
Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…