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<p>Pourbaix diagrams are a widespread tool in electrochemistry, enabling a direct understanding of how phase stability changes with potential and pH. While originally founded on a combination of theoretical equations and experimental data, in recent years, there has been a growing interest in the <i>prediction</i> of this kind of diagram by means of computational data, fueled by the success of massive databases such as the Materials Project. Herein, we propose a novel approach based on the calculation of thermodynamic Boltzmann populations across the pH and potential space, constructing 3D <i>Pourbaix spaces</i>. From there, we introduce competition-informed Pourbaix diagrams, where the representation of the <i>prevalence</i> of the major phase reveals regions where multiple phases are nearly degenerate, thereby identifying conditions where phase assignments are ill-defined, as energy differences between phases fall inside the DFT standard error range—providing thermodynamic confidence in the assignment of dominant phases across the diagram. Furthermore, we propose additional analyses on the computed populations to improve the characterization of competitive phases in regions of interest (speciation diagrams) and to simplify the comparison of inter-system Pourbaix diagrams. We implement this framework in PourbaixUP, an open-source tool enabling effective and detailed analysis of binary, ternary and higher-order systems, which demonstrate through several examples: binary metal combinations, passivation and corrosion windows in Fe/Cr alloys and chalcogenide materials of interest in thermoelectricity.</p>

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Enriching Pourbaix diagrams for improved understanding of competitive electrochemical phases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Diego Garay-Ruiz,
  • Marisol Martín-González

摘要

Pourbaix diagrams are a widespread tool in electrochemistry, enabling a direct understanding of how phase stability changes with potential and pH. While originally founded on a combination of theoretical equations and experimental data, in recent years, there has been a growing interest in the prediction of this kind of diagram by means of computational data, fueled by the success of massive databases such as the Materials Project. Herein, we propose a novel approach based on the calculation of thermodynamic Boltzmann populations across the pH and potential space, constructing 3D Pourbaix spaces. From there, we introduce competition-informed Pourbaix diagrams, where the representation of the prevalence of the major phase reveals regions where multiple phases are nearly degenerate, thereby identifying conditions where phase assignments are ill-defined, as energy differences between phases fall inside the DFT standard error range—providing thermodynamic confidence in the assignment of dominant phases across the diagram. Furthermore, we propose additional analyses on the computed populations to improve the characterization of competitive phases in regions of interest (speciation diagrams) and to simplify the comparison of inter-system Pourbaix diagrams. We implement this framework in PourbaixUP, an open-source tool enabling effective and detailed analysis of binary, ternary and higher-order systems, which demonstrate through several examples: binary metal combinations, passivation and corrosion windows in Fe/Cr alloys and chalcogenide materials of interest in thermoelectricity.