Predicting aqueous stability of solid with computed Pourbaix diagram using SCAN functional
Source abstract
Abstract
Abstract In this work, using the SCAN functional, we develop a simple method on top of the Materials Project (MP) Pourbaix diagram framework to accurately predict the aqueous stability of solids. We extensively evaluate the SCAN functional’s performance in computed formation enthalpies for a broad range of oxides and develop Hubbard U corrections for transition-metal oxides where the standard SCAN functional exhibits large deviations. The performance of the calculated Pourbaix diagram using the SCAN functional is validated with comparison to the experimental and the MP PBE Pourbaix diagrams for representative examples. Benchmarks indicate the SCAN Pourbaix diagram systematically outperforms the MP PBE in aqueous stability prediction. We further show applications of this method in accurately predicting the dissolution potentials of the state-of-the-art catalysts for oxygen evolution reaction in acidic media.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Computational Drug Discovery Methods · Thermal and Kinetic Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
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