Learning in higher dimensions: a strategy for alloy electrocatalyst discovery.

ABSTRACT: In this work, we demonstrate the inversion of the classical bottom-up approach to drive the discovery of improved energy conversion electrocatalysts top-down. Starting with complex alloy catalysts of many constituents, we down-select to optimal materials by removing low-performing elements from the alloy. The efficiency of this data-driven approach arises from the fact that when studying many elements together in one material, information is also obtained on the less complex alloys that contain fewer constituents. Therefore, the number of experiments required to study the complex alloy is fewer than those needed for studying all constituent alloys individually. In addition, this top-down approach allows for a new way of comparing activity models constructed from experimental data with theo

SUBMITTER: Mints VA 

PROVIDER: S-EPMC12805875 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature