A Flexible Theory for Catalysis: Learning Alkaline Oxygen Reduction on Complex Solid Solutions within the Ag-Pd-Pt-Ru Composition Space.

Compositionally complex materials such as high-entropy alloys and oxides have the potential to be efficient platforms for catalyst discovery because of the vast chemical space spanned by these novel materials. Identifying the composition of the most active catalyst materials, however, requires unraveling the descriptor-activity relationship, as experimentally screening the multitude of possible element ratios quickly becomes a daunting task. In this work, we show that inferred adsorption energy distributions of *OH and *O on complex solid solution surfaces within the space spanned by the system Ag-Pd-Pt-Ru are coupled to the experimentally observed electrocatalytic performance for the oxygen reduction reaction. In total, the catalytic activity of 1582 alloy compositions is predicted with a cross-validated mean absolute error of 0.042 mA/cm2 by applying a theory-derived model with only two adjustable parameters. Trends in the discrepancies between predicted electrochemical performance values of the model and the measured values on thin film surfaces subsequently provide insight into the alloys' surface compositions during reaction conditions. Bridging this gap between computationally modeled and experimentally observed catalytic activities, not only reveals insight into the underlying theory of catalysis but also takes a step closer to realizing exploration and exploitation of high-entropy materials.

Chemical Insights into the Formation of Colloidal Iridium Nanoparticles from In Situ X-ray Total Scattering: Influence of Precursors and Cations on the Reaction Pathway.

Iridium nanoparticles are important catalysts for several chemical and energy conversion reactions. Studies of iridium nanoparticles have also been a key for the development of kinetic models of nanomaterial formation. However, compared to other metals such as gold or platinum, knowledge on the nature of prenucleation species and structural insights into the resultant nanoparticles are missing, especially for nanoparticles obtained from IrxCly precursors investigated here. We use in situ X-ray total scattering (TS) experiments with pair distribution function (PDF) analysis to study a simple, surfactant-free synthesis of colloidal iridium nanoparticles. The reaction is performed in methanol at 50 °C with only a base and an iridium salt as precursor. From different precursor salts─IrCl3, IrCl4, H2IrCl6, or Na2IrCl6─colloidal nanoparticles as small as Ir∼55 are obtained as the final product. The nanoparticles do not show the bulk iridium face-centered cubic (fcc) structure but show decahedral and icosahedral structures. The formation route is highly dependent on the precursor salt used. Using IrCl3 or IrCl4, metallic iridium nanoparticles form rapidly from IrxClyn- complexes, whereas using H2IrCl6 or Na2IrCl6, the iridium nanoparticle formation follows a sudden growth after an induction period and the brief appearance of a crystalline phase. With H2IrCl6, the formation of different Irn (n = 55, 55, 85, and 116) nanoparticles depends on the nature of the cation in the base (LiOH, NaOH, KOH, or CsOH, respectively) and larger particles are obtained with larger cations. As the particles grow, the nanoparticle structure changes from partly icosahedral to decahedral. The results show that the synthesis of iridium nanoparticles from IrxCly is a valuable iridium nanoparticle model system, which can provide new compositional and structural insights into iridium nanoparticle formation and growth.

Breaking with the Principles of Coreduction to Form Stoichiometric Intermetallic PdCu Nanoparticles.

Intermetallic nanoparticles (NPs) have shown enhanced catalytic properties as compared to their disordered alloy counterparts. To advance their use in green energy, it is crucial to understand what controls the formation of intermetallic NPs over alloy structures. By carefully selecting the additives used in NP synthesis, it is here shown that monodisperse, intermetallic PdCu NPs can be synthesized in a controllable manner. Introducing the additives iron(III) chloride and ascorbic acid, both morphological and structural control can be achieved. Combined, these additives provide a synergetic effect resulting in precursor reduction and defect-free growth; ultimately leading to monodisperse, single-crystalline, intermetallic PdCu NPs. Using in situ X-ray total scattering, a hitherto unknown transformation pathway is reported that diverges from the commonly reported coreduction disorder-order transformation. A Cu-rich structure initially forms, which upon the incorporation of Pd(0) and atomic ordering forms intermetallic PdCu NPs. These findings underpin that formation of stoichiometric intermetallic NPs is not limited by standard reduction potential matching and coreduction mechanisms, but is instead driven by changes in the local chemistry. Ultimately, using the local chemistry as a handle to tune the NP structure might open new opportunities to expand the library of intermetallic NPs by exploiting synthesis by design.

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*.

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys. This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

What Atomic Positions Determines Reactivity of a Surface? Long-Range, Directional Ligand Effects in Metallic Alloys.

Ligand and strain effects can tune the adsorption energy of key reaction intermediates on a catalyst surface to speed up rate-limiting steps of the reaction. As novel fields like high-entropy alloys emerge, understanding these effects on the atomic structure level is paramount: What atoms near the binding site determine the reactivity of the alloy surface? By statistical analysis of 2000 density functional theory calculations and subsequent host/guest calculations, it is shown that three atomic positions in the third layer of an fcc(111) metallic structure fourth-nearest to the adsorption site display significantly increased influence on reactivity over any second or third nearest atomic positions. Subsequently observed in multiple facets and host metals, the effect cannot be explained simply through the d-band model or a valence configuration model but rather by favorable directions of interaction determined by lattice geometry and the valence difference between host and guest elements. These results advance the general understanding of how the electronic interaction of different elements affect adsorbate-surface interactions and will contribute to design principles for rational catalyst discovery of better, more stable and energy efficient catalysts to be employed in energy conversion, fuel cell technologies, and industrial processes.

Complex-Solid-Solution Electrocatalyst Discovery by Computational Prediction and High-Throughput Experimentation*.

Complex solid solutions ("high entropy alloys"), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data-driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition-spread thin-film libraries are synthesised. High-throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru. The method can identify optimal complex-solid-solution materials for electrocatalytic reactions in an unprecedented manner.