Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.

搜集汇总

背景与挑战

背景概述

该数据集HEA25S专注于高熵合金（HEAs）的表面偏析研究，采用化学机器学习方法，包含大量体相结构、表面板及分子动力学快照数据，用于分析多元素合金中不同元素在表面的偏析趋势。数据集还提供了用于研究的机器学习模型，支持高熵合金在催化剂等领域的开发应用。