AQ-GNoME: an Aqueous Stability Augmentation of the GNoME Database

Abstract

Aqueous stability is a fundamental requirement for materials used in electrochemical energy technologies, yet no systematic Pourbaix stability screening has been performed for Google DeepMind’s GNoME database of more than 520,000 novel inorganic crystals. Here, we introduce AQ-GNoME, an aqueous-stability augmentation of the GNoME database, enabling systematic shortlisting of GNoME candidates for electrochemical applications. The database additionally includes machine-learning-based substitutional-disorder predictions and Herfindahl–Hirschman Index supply-risk metrics, thereby supporting structural-order and supply-risk based filtering. User-defined criteria across electrochemical potential and pH, elemental composition, structural order, and supply concentration reduce the database to a manageable, project-specific shortlist. We apply AQ-GNoME to identify GNoME materials relevant for further investigation in electrochemical CO2 reduction (eCO2RR) and acidic oxygen evolution (OER), focusing on candidates absent from the Materials Project and ICSD. For eCO2RR, combining aqueous stability criteria with a compositional heuristic intended to mimic copper’s selectivity, we identify 8 ordered multimetallic candidates. For acidic OER, combining stability and supply-concentration criteria, we identify 39 Ir-lean oxides, of which 26 are metallic under HSE06 DFT. AQ-GNoME therefore provides a practical framework for electrochemical materials discovery within GNoME, reducing the search space from more than half a million compounds to experimentally tractable candidate sets.

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BibTeX RIS

BibTeX

@misc{nissen2026aqgnome,
  title = {{AQ-GNoME: an Aqueous Stability Augmentation of the GNoME Database}},
  author = {Mathias S. Nissen and Peter Beck and Leo Karlsson and Manuel Dillenz and Alexander Bagger and Ivano E. Castelli and Heine A. Hansen and Heine Anton Hansen},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15003677/v2},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003677/v2},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - AQ-GNoME: an Aqueous Stability Augmentation of the GNoME Database
AU  - Mathias S. Nissen
AU  - Peter Beck
AU  - Leo Karlsson
AU  - Manuel Dillenz
AU  - Alexander Bagger
AU  - Ivano E. Castelli
AU  - Heine A. Hansen
AU  - Heine Anton Hansen
PY  - 2026
DA  - 2026/09/17
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15003677/v2
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15003677/v2
AB  - Aqueous stability is a fundamental requirement for materials used in electrochemical energy technologies, yet no systematic Pourbaix stability screening has been performed for Google DeepMind’s GNoME database of more than 520,000 novel inorganic crystals. Here, we introduce AQ-GNoME, an aqueous-stability augmentation of the GNoME database, enabling systematic shortlisting of GNoME candidates for electrochemical applications. The database additionally includes machine-learning-based substitutional-disorder predictions and Herfindahl–Hirschman Index supply-risk metrics, thereby supporting structural-order and supply-risk based filtering. User-defined criteria across electrochemical potential and pH, elemental composition, structural order, and supply concentration reduce the database to a manageable, project-specific shortlist. We apply AQ-GNoME to identify GNoME materials relevant for further investigation in electrochemical CO2 reduction (eCO2RR) and acidic oxygen evolution (OER), focusing on candidates absent from the Materials Project and ICSD. For eCO2RR, combining aqueous stability criteria with a compositional heuristic intended to mimic copper’s selectivity, we identify 8 ordered multimetallic candidates. For acidic OER, combining stability and supply-concentration criteria, we identify 39 Ir-lean oxides, of which 26 are metallic under HSE06 DFT. AQ-GNoME therefore provides a practical framework for electrochemical materials discovery within GNoME, reducing the search space from more than half a million compounds to experimentally tractable candidate sets.
ER  -

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