Christian Møgelberg Clausen

Multimetallic nanoparticles (NPs) offer access to a vast compositional and architectural design space, yet controlled formation of core-shell and multi-shell architectures rapidly becomes unpredictable as compositional complexity increases. As a result, architectural outcomes are often regarded as system-specific and governed primarily by kinetic effects. Here, we identify a unifying thermodynamic principle that governs shell formation in multimetallic NPs based on binary mixing enthalpies and phase behaviour. By systematically establishing enthalpy-driven rules in binary NPs and extending them to higher-order compositions, we show that elemental segregation and the composition and sequence of multiple shells in NPs containing up to five elements are predictable despite their combinatorial complexity, even when synthesised in a simple one-pot wet-chemical reaction. These results demonstrate that thermodynamic interactions maintain predictive power as compositional complexity increases and provide a general framework for rationalising architectural design in multimetallic NPs.

Medium-throughput exploration of Pd–Ag–Au compositional space for ORR and the comparison between experimental and DFT calculation models.

We present a medium‐throughput synthesis approach for High Entropy Alloy (HEA) noble metal nanoparticles on conductive supports via electrodeposition. The presented method utilizes aqueous electrolyte solutions, exploiting high overpotentials to achieve mass transport‐controlled deposition. This ensures electrodeposition independent from individual equilibrium potentials of the different elements. Hydrogen evolution which interferes with electrodeposition is suppressed by operating in a pulsed mode at a mildly acidic pH. Applying the approach to the Au–Ir–Pt–Pd–Rh–Ru composition space, this study demonstrates that the developed method is fast, adaptable, and enables compositional control while maintaining a homogeneous element distribution. The mechanism of HEA nanoparticle synthesis is further investigated by examining material‐specific seed formation and diffusion phenomena. The results indicate that together with the electrolyte composition, seed formation as well as the diffusion of metal precursors in the aqueous phase govern the average composition of the synthesized HEA nanoparticles, while the formation enthalpies of element pairs explain the atomic‐scale segregation observed.

High-entropy alloys are increasingly recognised as promising catalyst materials due to their highly tunable compositions. In particular, the Cantor alloy, CrCoFeMnNi, has attracted significant interest as a low-cost, earth-abundant alternative to Pt-group catalysts. However, the Cantor alloy is susceptible to degradation in acidic environments. To address this, Pt has been proposed as an addition to the Cantor alloy to leverage the high catalytic activity and surface stability of Pt. In this study, we identify and elucidate the catalytic activity trend for the oxygen reduction reaction (ORR) by screening CrCoFeMnNiPt alloys containing 50–100 at.% Pt and up to 50 at.% of each 3d element at 10 at.% resolution using a purely computational, machine learning-accelerated density-functional-theory workflow. Assuming the formation of a Pt skin through surface reconfiguration, a machine learning-accelerated simulation protocol infers the strain-corrected adsorption strengths of key reaction intermediates and uses them as descriptors for kinetic modelling. Our results show that Pt-modification of the Cantor alloy is a potentially viable strategy towards active and durable ORR catalysts. However, the simulations also predict binary Pt-rich alloys with approximately 80 at.% Pt to exhibit the highest ORR activity due to synergistic ligand and strain effects. As this model identifies known alloys with high ORR performance, we suggest that this framework contributes to a clear understanding of how bulk composition affects the reaction kinetics of complex surface alloys.

High-entropy alloys (HEAs) are increasingly recognised as promising catalyst materials due to their highly tunable compositions. In particular, the Cantor alloy, CrCoFeMnNi, has attracted significant interest as a low-cost, earth-abundant alternative to Pt-group catalysts. However, the Cantor alloy is susceptible to degradation in acidic environments. To address this, Pt has been proposed as an addition to the Cantor alloy to leverage the high catalytic activity and surface stability of Pt. In this study, we identify and elucidate the catalytic activity trend for the oxygen reduction reaction (ORR) across the CrCoFeMnNiPt composition space. Assuming the formation of a Pt skin through surface reconfiguration, a machine learning-accelerated simulation protocol infers the strain-corrected adsorption strengths of key reaction intermediates and uses them as descriptors for kinetic modelling. Our results show that Pt-modification of the Cantor alloy is a potentially viable strategy towards active and durable ORR catalysts. However, we also observe binary Pt-rich alloys with approximately 80% Pt exhibit the highest ORR activity due to synergistic ligand and strain effects. As this analysis identifies known alloys with high ORR performance, we suggest that this framework contributes to a clear understanding of how bulk composition affects the reaction kinetics of complex surface alloys.

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.

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.

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 experimental 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.

High‐entropy alloys (HEAs) represent near‐equimolar points in the middle of a vast composition space of multi‐metallic catalysts. Successful modeling of the catalytic activity of these complex materials allows to search this composition space for optimal catalysts. This study shows the effect of approximating the ligand effect of the surrounding atoms around an adsorption site with a mean‐field perturbation. Modeling the electrocatalytic activity of the oxygen reduction reaction on the quinary AgIrPdPtRu HEA, it is shown that the extent of such a mean‐field approximation is valid up to and including equimolar ternary alloys, corresponding to 60.3 % of the quinary composition space, by comparing to models that consider the ligand effect locally. When extrapolating to make predictions far from near‐equimolar compositions, such as for binary alloys, the mean field has been sufficiently perturbed to cause large discrepancies. Here, the local ligand models thus prove more useful for discovering optimal catalysts.