Computational Progress of High Entropy Materials

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High entropy materials (HEMs) are distinct for their varied compositions involving multiple elements in nearly equal amounts, unlike conventional materials that typically rely on one or two main elements with smaller additives. They cover a wide spectrum of materials, including high entropy alloys (HEAs), ceramics (HECs), and oxides (HEOs), offering customizable properties—a thrilling arena in materials science.

Computational methods play a vital role in the study of HEMs because of their vast composition space and intricate atomic interactions. They are instrumental in predicting properties, minimizing the need for trial-and-error experiments, and providing insights into their stability and transformation processes. Recent advancements in computational techniques, such as machine learning and multi-scale modeling, have transformed the field of HEMs research. These approaches enable the prediction of novel structures, simulation of atomic behavior, and integration of experimental data with computational analysis, leading to a deeper understanding of HEMs.

We invite submission of papers focusing on the computational advancements of high entropy materials. All submitted papers will be subjected to a rigorous peer-review process and adhere to the same editorial standards as regular npj Computational Materials articles. The Guest Editors overseeing the submissions declare no competing interests and will maintain impartiality throughout the peer-review process.

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Algorithm programming and artificial intelligence