Heterogeneous bimetallic catalysts have broad applications in industrial processes, but
achieving a fundamental understanding on the nature of the active sites in bimetallic …

The essential demand for functional materials enabling the realization of new energy
technologies has triggered tremendous efforts in scientific and industrial research in recent …

Entropy-related phase stabilization can allow compositionally complex solid solutions of
multiple principal elements. The massive mixing approach was originally introduced for …

High-entropy alloys (HEA) are of high current interest due to their unique and attractive
combination of structural, physical, chemical or magnetic properties. HEA comprise multiple …

The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …

The broad compositional space of high entropy alloys (HEA) is conducive to the design of
HEAs with targeted performance. Herein, a data-driven and machine learning (ML) assisted …

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with
mean-field methods to accelerate assessment of technologically useful properties of high …

The mechanical properties of a directionally solidified (DS) TiAl alloy were predicted through
a random forest regression (RFR) machine learning algorithm. The prediction results were …

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical
properties for elevated temperature applications by employing machine learning (ML) in …

This work evaluated the phase prediction capability of high entropy alloys using four
supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression …