The Reverse Pourbaix Explorer: a condition-first screening tool of aqueous stability on the Materials Project

Aqueous stability is a critical constraint in the discovery of materials for electrocatalysis, corrosion-resistant systems, and electrochemical energy technologies. Conventional Pourbaix analysis follows a material-first workflow in which a predefined composition is evaluated across pH and electrochemical potential. Here, we introduce the Reverse Pourbaix Explorer, an interactive, condition-first screening application hosted on the Materials Project. This tool identifies structures predicted to be stable at user-selected aqueous operating conditions using a pre-computed dataset of Pourbaix decomposition energies evaluated across pH and potential. Users select an operating condition and stability tolerance from an interactive heatmap and retrieve a filterable list of candidate structures. The application further integrates adsorptionenergy data from the Open Catalyst Project, enabling candidates to be screened jointly according to aqueous stability and catalytic binding-energy descriptors. We demonstrate the application’s utility with two examples: stable, conductive materials under acidic OER conditions, and CO2 reduction candidates screened by stability, conductivity and adsorption energy. These examples show how the application can reduce a large materials database to tractable candidate sets while retaining direct links to the underlying structures, Pourbaix analyses and adsorption configurations for further scrutiny. However, the predictions remain limited by thermodynamic approximations. The Reverse Pourbaix Explorer application lowers the technical barrier to large-scale aqueous stability screening and provides an extensible platform for data-driven materials and catalyst discovery.

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Publication Details

Journal
ChemRxiv
Published
2026-11-11
DOI
https://doi.org/10.26434/chemrxiv.15011970/v1
Primary Topic
Machine Learning in Materials Science
Type
preprint
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preprint

The Reverse Pourbaix Explorer: a condition-first screening tool of aqueous stability on the Materials Project

Patrick Huck, Kristin Aslaug Persson, Lisbeth Karlsson, Min–Hsueh Chiu et al.
ChemRxiv
Machine Learning in Materials Science
preprint

The Reverse Pourbaix Explorer: a condition-first screening tool of aqueous stability on the Materials Project

Patrick Huck, Kristin Aslaug Persson, Lisbeth Karlsson, Min–Hsueh Chiu, Tyler S. Mathis, Alexander Bagger, Mathias Stokkebye Nissen, Peter Beck, Brendan Foley
preprint en

Abstract

Aqueous stability is a critical constraint in the discovery of materials for electrocatalysis, corrosion-resistant systems, and electrochemical energy technologies. Conventional Pourbaix analysis follows a material-first workflow in which a predefined composition is evaluated across pH and electrochemical potential. Here, we introduce the Reverse Pourbaix Explorer, an interactive, condition-first screening application hosted on the Materials Project. This tool identifies structures predicted to be stable at user-selected aqueous operating conditions using a pre-computed dataset of Pourbaix decomposition energies evaluated across pH and potential. Users select an operating condition and stability tolerance from an interactive heatmap and retrieve a filterable list of candidate structures. The application further integrates adsorptionenergy data from the Open Catalyst Project, enabling candidates to be screened jointly according to aqueous stability and catalytic binding-energy descriptors. We demonstrate the application’s utility with two examples: stable, conductive materials under acidic OER conditions, and CO2 reduction candidates screened by stability, conductivity and adsorption energy. These examples show how the application can reduce a large materials database to tractable candidate sets while retaining direct links to the underlying structures, Pourbaix analyses and adsorption configurations for further scrutiny. However, the predictions remain limited by thermodynamic approximations. The Reverse Pourbaix Explorer application lowers the technical barrier to large-scale aqueous stability screening and provides an extensible platform for data-driven materials and catalyst discovery.

ChemRxiv
Lawrence Berkeley National Laboratory (US), Technical University of Denmark (DK)
Machine Learning in Materials Science
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