Authors: Raul Flores | Christopher Paolucci | Kirsten T. Winther | Ankit Jain | José Antonio Garrido Torres | Muratahan Aykol | Joseph H. Montoya | Jens K. Nørskov | Michal Bajdich | Thomas Bligaard
Source: ChemRxiv (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO&lt;sub&gt;2&lt;/sub&gt; and IrO&lt;sub&gt;3&lt;/sub&gt;. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB&lt;sub&gt;2&lt;/sub&gt; and AB&lt;sub&gt;3&lt;/sub&gt; prototypes in existing materials databases (more than 38000). Next, using an active learning approach we are able to find 196 IrO&lt;sub&gt;2&lt;/sub&gt; polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO&lt;sub&gt;3&lt;/sub&gt; polymorphs and report a previously unknown FeF&lt;sub&gt;3&lt;/sub&gt;-type structure as the most stable, termed α-IrO&lt;sub&gt;3&lt;/sub&gt;. To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO&lt;sub&gt;2&lt;/sub&gt; and IrO&lt;sub&gt;3&lt;/sub&gt; with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H&lt;sub&gt;2&lt;/sub&gt;O analysis shows that α-IrO&lt;sub&gt;3&lt;/sub&gt; is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO&lt;sub&gt;2&lt;/sub&gt;. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO&lt;sub&gt;3&lt;/sub&gt; than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry. Amorphous solid ; Oxygen evolution ; Chemical space ; Chemistry ; Rutile ; Materials science ; Crystallography ; Nanotechnology ; Electrochemistry ; Physical chemistry ; Drug discovery Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electrocatalysts for Energy Conversion