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.
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BibTeX
@misc{zang2026sustainabilityaware,
title = {{Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost}},
author = {Yanmei Zang and Hyun Gyu Park and Gi Beom Sim and Tae Hyeon Park and Ho Jin Lee and Xiaorong Zou and D. ChangMo Yang and Soohaeng Yoo Willow and Hye Jung Kim and Sunbin Yoo and Chang Woo Myung},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.29821},
eprint = {2605.29821},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.29821},
}
RIS
TY - UNPB
TI - Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost
AU - Yanmei Zang
AU - Hyun Gyu Park
AU - Gi Beom Sim
AU - Tae Hyeon Park
AU - Ho Jin Lee
AU - Xiaorong Zou
AU - D. ChangMo Yang
AU - Soohaeng Yoo Willow
AU - Hye Jung Kim
AU - Sunbin Yoo
AU - Chang Woo Myung
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2605.29821
UR - https://arxiv.org/abs/2605.29821
AB - 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.
ER -