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 IrO2 and IrO3. 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
AB2 and AB3 prototypes in existing materials
databases (more than 38000). Next, using an active learning approach,
we find 196 IrO2 polymorphs within the thermodynamic amorphous
synthesizability limit and reaffirm the global stability of the rutile
structure. We find 75 synthesizable IrO3 polymorphs and
report a previously unknown FeF3-type structure as the
most stable, termed α-IrO3. To test the algorithms
performance, we compare to a random search of the candidate space
and report at least a 2-fold increase in the rate of discovery. Additionally,
the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer 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–H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and
supersedes the stability of rutile IrO2. Calculation of
theoretical OER surface activities reveal ideal weaker binding of
the OER intermediates on α-IrO3 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.