The search for better and cheaper electrocatalysts is vital in the global transition to renewable energy resources. High-entropy alloys (HEAs) provide a near-infinite number of different alloys with approximately continuous properties such as catalytic activity. In this work, the catalytic activity for the electrochemical oxygen reduction reaction as a function of molar composition of Ag-Ir-Pd-Pt-Ru HEA is treated as a landscape wherein it is shown that the maxima are connected through ridges. By following the ridges, it is possible to navigate between the maxima using a modified nudged elastic band (NEB) model integrated in a machine learning NEB algorithm. These results provide a new understanding of the composition space being similar to an evolutionary landscape. This provides a possible new search and design strategy for new catalysts in which the composition of known catalysts can be optimized by following ridges rather than exploring the whole alloy composition space.

中文翻译：

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追踪高熵合金成分空间中最大催化活性的路径

寻找更好、更便宜的电催化剂对于全球向可再生能源的过渡至关重要。高熵合金 (HEA) 提供了几乎无限数量的不同合金，它们具有近似连续的特性，例如催化活性。在这项工作中，作为 Ag-Ir-Pd-Pt-Ru HEA 摩尔组成函数的电化学氧还原反应的催化活性被视为景观，其中显示最大值通过脊连接。通过跟随脊线，可以使用集成在机器学习 NEB 算法中的改进的微动弹性带 (NEB) 模型在最大值之间导航。这些结果提供了对类似于进化景观的构图空间的新理解。