Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set.
In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.

由多种元素按近等摩尔比例组成的合金，通常被称为高熵合金，因其热力学性质及独特的力学性能而长期受到关注，近年来，它们在催化领域的潜在应用亦引起了广泛关注。此类合金对于传统的原子建模以及大部分具有记忆足迹、计算成本和数据需求随元素数量增加而急剧上升的数据驱动势能模型构成了相当大的挑战。本研究应用了一种近期提出的化学信息降维压缩方案，显著降低了模型成本，同时精度损失可忽略不计，从而构建了一个能够描述25种d区过渡金属的势能模型。该模型在典型合金的描述上展现出半定量精度，且在扩展至训练集之外的结构时表现出惊人的稳定性。在本记录中，我们提供了一组包含25,000个结构的数据库，这些结构用于上述势能模型的拟合，重点关注25种d区过渡金属，不包括Tc、Cd、Re、Os和Hg。