Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution.

The demand for green hydrogen has raised concerns over the availability of iridium used in oxygen evolution reaction catalysts. We identify catalysts with the aid of a machine learning-aided computational pipeline trained on more than 36,000 mixed metal oxides. The pipeline accurately predicts Pourbaix decomposition energy (Gpbx) from unrelaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic oxides with respect to their prospective stability under acidic conditions. The search identifies Ru0.6Cr0.2Ti0.2O2 as a candidate having the promise of increased durability: experimentally, we find that it provides an overpotential of 267 mV at 100 mA cm-2 and that it operates at this current density for over 200 h and exhibits a rate of overpotential increase of 25 µV h-1. Surface density functional theory calculations reveal that Ti increases metal-oxygen covalency, a potential route to increased stability, while Cr lowers the energy barrier of the HOO* formation rate-determining step, increasing activity compared to RuO2 and reducing overpotential by 40 mV at 100 mA cm-2 while maintaining stability. In situ X-ray absorption spectroscopy and ex situ ptychography-scanning transmission X-ray microscopy show the evolution of a metastable structure during the reaction, slowing Ru mass dissolution by 20× and suppressing lattice oxygen participation by >60% compared to RuO2.

WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions.

This paper introduces WhereWulff, a semiautonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initial bulk structure and returns the optimized bulk geometry and magnetic state, including stability under reaction conditions. The stable bulk structure is the input to a surface chemistry task that enumerates surfaces up to a user-specified maximum Miller index, computes relaxed surface energies for those surfaces, and then prioritizes those for subsequent adsorption energy calculations based on their contribution to the Wulff construction shape. The workflow handles computational resource constraints such as limited wall-time as well as automated job submission and analysis. We illustrate the workflow for oxygen evolution reaction (OER) intermediates on two double perovskites. WhereWulff nearly halved the number of Density Functional Theory (DFT) calculations from ∼240 to ∼132 by prioritizing terminations, up to a maximum Miller index of 1, based on surface stability. Additionally, it automatically handled the 180 additional resubmission jobs required to successfully converge 120+ atoms systems under a 48-h wall-time cluster constraint. There are four main use cases that we envision for WhereWulff: (1) as a first-principles source of truth to validate and update a closed-loop self-sustaining materials discovery pipeline, (2) as a data generation tool, (3) as an educational tool, allowing users (e.g., experimentalists) unfamiliar with OER modeling to probe materials they might be interested in before doing further in-domain analyses, (4) and finally, as a starting point for users to extend with reactions other than the OER, as part of a collaborative software community.