Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost

Abstract

Catalyst discovery for green hydrogen has optimized activity alone, deferring economic and environmental costs to stages where they disqualify otherwise promising candidates. Here we introduce a unified machine learning framework that screens catalytic activity and monetized carbon cost in a single pipeline. A machine learning potential predicts hydrogen evolution activity and stability at density-functional-theory accuracy. A second model, trained on published estimates of the social cost of carbon, converts each candidate's embodied CO2 into a monetary burden. Screening 460 graphene-supported dual-atom catalysts across 23 transition metals and 20 nitrogen-coordination motifs yields 26 active, synthesizable candidates. The two axes then conflict. The most active candidates span Mn-, Fe-, Zr-, Cr-, Cu-, Ni-, Ti-, Nb-, V-, Pd-, Au-, and Rh-based catalysts, indistinguishable in adsorption energy yet spanning a 58,000-fold range in carbon cost. Joint optimization selects Mn2@2Nf and Zr2@2Nc, matching the best precious-metal activity at tens-of-thousands-fold lower carbon cost, independent of catalyst loading.

arXiv commentResearch article, 61 pages, 21 figures