High-entropy alloys (HEAs), characterized as compositionally complex solid solutions with five or more metal elements, have emerged as a novel class of catalytic materials with unique attributes. Because of the remarkable diversity of multielement sites or site ensembles stabilized by configurational entropy, human exploration of the multidimensional design space of HEAs presents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-based modeling. Leveraging deep learning interatomic potentials for large-scale molecular simulations and pretrained machine learning models of surface reactivity, our approach effectively rationalizes the enhanced activity of a previously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemical oxygen reduction, as corroborated by experimental observations. We contend that this framework deepens our fundamental understanding of the surface reactivity of high-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatile material platform for catalyzing sustainable chemical and energy transformations.

中文翻译：

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利用计算和数据驱动的方法揭示高熵合金电催化剂氧还原的反应起源


高熵合金（HEA）的特点是具有五种或更多金属元素的成分复杂的固溶体，已成为一类具有独特属性的新型催化材料。由于构型熵稳定的多元素位点或位点集合具有显着的多样性，人类对 HEA 多维设计空间的探索提出了巨大的挑战，需要一种比传统试错实验或物理方法更高效、计算和数据驱动的策略基于建模。利用深度学习原子间势进行大规模分子模拟和预先训练的表面反应性机器学习模型，我们的方法有效地合理化了先前合成的 PdCuPtNiCo HEA 纳米颗粒系统用于电化学氧还原的增强活性，实验观察证实了这一点。我们认为，该框架加深了我们对高熵材料表面反应性的基本理解，并促进单分散 HEA 纳米粒子的加速开发和合成，作为催化可持续化学和能源转化的多功能材料平台。