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.高熵合金（High-entropy Alloys, HEAs）由数种近等摩尔比的金属元素构成，其独特的力学性能长期以来备受学界关注。近年来，凭借广阔的设计空间与组元间的协同效应，高熵合金已成为开发新型多相催化剂的极具潜力的研究平台。本研究采用可同时建模最多25种过渡金属（d区过渡金属，排除锝[Tc]、镉[Cd]、铼[Re]、锇[Os]与汞[Hg]）的机器学习势函数，探究不同元素在高熵合金表面的偏析趋势。本数据集包含HEA25S数据集，其中涵盖10000个高熵合金块体结构（数据集O）、2640个高熵合金表面晶胞（数据集A）；另有1000个块体与1000个表面晶胞的分子动力学（Molecular Dynamics, MD）模拟快照（数据集B与C），以及500个含25种元素的康托尔型合金表面晶胞的MD模拟快照。本研究还提供了研究中使用的HEA25-4-NN与HEA25S-4-NN最终模型。数据集与模型的完整说明可参见下文的参考文献。

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。