arXiv subjects

Wolfgang Schuhmann

Publications and source records attributed to Wolfgang Schuhmann.

7 recordsLinked to original sources

arXiv2026

Work Function and High-Coverage Adsorption Energy as Hydrogen-Evolution Descriptors on Ag-Au-Pd-Pt Alloys

Hydrogen-evolution activity is commonly rationalized through hydrogen adsorption energies and the Sabatier principle, yet this descriptor picture becomes ambiguous on multimetallic surfaces, where each composition exposes a distribution of local adsorption environments. Here we investigate whether the bare-surface work function, previously shown to add predictive information for monometallic surfaces, remains an activity descriptor for compositionally complex surfaces. We test this on three combinatorial Ag-Au-Pd-Pt thin-film materials libraries screened for acidic hydrogen evolution by scanning electrochemical cell microscopy. Graph neural networks provide adsorption-energy distributions and work functions for each measured composition. A work-function-only model explained most of the activity variation (mean $R^2_\mathrm{log}$ = 0.903), as did a coverage-corrected adsorption model (mean $R^2_\mathrm{log}$ = 0.955), outperforming dilute adsorption (mean $R^2_\mathrm{log}$ = 0.758). Combining work function and coverage-corrected adsorption yielded the highest fit quality (mean $R^2_\mathrm{log}$ = 0.969), but only a small gain over coverage-corrected adsorption alone. For these four metals the coverage-corrected adsorption energy and work function follow a similar trend, producing similar activity rankings, hence including both adds little beyond either one individually, although both are strong predictors.

cond-mat.mtrl-sci↗

arXiv2026

Autonomous scanning electrochemical cell microscopy enables rapid exploration of large compositionally complex material spaces

Alloying is a central strategy in electrocatalysis, enabling fine-tuning of electronic structure. In particular, compositionally complex solid solutions (CCSS) often called high-entropy alloys are of high interest as they allow active site design. However, the "combinatorial explosion" in the number of possible compositions poses a critical bottleneck for the discovery of active CCSS electrocatalysts. We present an autonomous scanning electrochemical cell microscopy (SECCM) system for ultrahigh-throughput and large-scale CCSS activity screening. The platform rapidly establishes composition-electrocatalytic activity relationships for large compositional spaces across multiple thin-film CCSS materials libraries via active learning and automated library exchange. Embedding analytical expressions of voltammetry in the algorithm enables the learning of whole voltammograms rather than a single selected metric. As a demonstration, we investigated hydrogen evolution reaction (HER) activities of Au-Ir-Rh, where Ir and Rh exhibit strong metal-hydrogen binding and Au exhibits relatively weak binding as derived from the HER volcano plot. The composition-activity trend was accurately predicted after measuring only 15% of all 966 measurement areas. Au30Ir20Rh50 and Au10Ir35Rh55 exhibit highest activities with standard rate constants of about 0.012 cm/s, demonstrating positive synergistic contributions from elemental mixing. The autonomous robotic SECCM platform is broadly applicable to a wide range of electrocatalytic reactions, providing a general pathway for accelerating CCSS electrocatalyst discovery and optimization.

cond-mat.mtrl-sci↗

arXiv2026

Influence of Ru content on electrocatalytic activity and defect formation of Au-Pd-Pt-Ru compositionally complex solid solution thin films

Compositionally complex solid solutions (CCSSs) consist of a randomly mixed single phase with the potential to enhance electrocatalytic activity through their polyelemental surface atom arrangements. However, microstructural complexity originating from multiple principal elements influences local structure, chemistry, and lattice strain, which might also affect electrocatalytic activity. Here, we investigate the effect of Ru content on electrochemistry and defect formation in Au-Pd-Pt-Ru CCSS thin films. Such defects could provide active sites when terminating at the CCSS surface or modify surface composition through preferential segregation. A thin-film material library covering a wide composition range was fabricated by room-temperature combinatorial co-sputtering. High-throughput compositional, structural and functional characterization, including electron microscopy equipped with energy dispersive X-ray spectroscopy, X-ray diffraction, and electrochemical screening, were used to correlate composition and microstructural features with catalytic activity. Three representative compositions selected from the library - Au68Pd13Pt15Ru4, Au27Pd24Pt23Ru26, and Au9Pd21Pt18Ru52 - were examined in detail. The three samples exhibit face-centered cubic structures, with lattice contraction occurring with increasing Ru content. In addition, with increasing Ru content, a transition from a high density of nanotwins to high-density, atomic-layer stacking faults was observed. Moreover, the hydrogen evolution reaction activity improves with higher Ru content. Atom probe tomography reveals local compositional fluctuations, including element-specific enrichment and depletion at grain boundaries. The findings provide a new insight into surface atom arrangement design in the CCSS electrocatalysts with enhanced performance.

cond-mat.mtrl-sci↗

arXiv2024

Composition-property extrapolation for compositionally complex solid solutions based on word embeddings

Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for the speedup in materials discovery. We show and discuss three models, using property data from two ternary systems (Ag-Pd-Ru; Ag-Pd-Pt), to predict material performance in the shared quaternary system (Ag-Pd-Pt-Ru). First, we apply Gaussian Process Regression (GPR) based on composition, which includes both Ag and Pd, achieving an initial correlation coefficient for the prediction ($r$) of 0.63 and a determination coefficient ($r^2$) of 0.08. Second, we present a version of the GPR model using word embedding-derived materials vectors as representations. Using materials-specific embedding vectors significantly improves the predictive capability, evident from an improved $r^2$ of 0.65. The third model is based on a `standard vector method' which synthesizes weighted vector representations of material properties, then creating a reference vector that results in a very good correlation with the quaternary system's material performance (resulting $r$ of 0.89). Our approach demonstrates that existing experimental data combined with latent knowledge of word embedding-based representations of materials can be used effectively for materials discovery where data is typically sparse.

cond-mat.mtrl-sci↗

arXiv2021

Combinatorial materials discovery strategy for high entropy alloy electrocatalysts using deposition source permutations

High entropy alloys offer a huge search space for new electrocatalysts. Searching for a global property maximum in one quinary system could require, depending on compositional resolution, the synthesis of up to 10E6 samples which is impossible using conventional approaches. Co-sputtered materials libraries address this challenge by synthesis of controlled composition gradients of each element. However, even such a materials library covers less than 1% of the composition space of a quinary system. We present a new strategy using deposition source permutations optimized for highest improvement of the covered new compositions. Using this approach, the composition space can be sampled in different subsections allowing identification of the contribution of individual elements and their combinations on electrochemical activity. Unsupervised machine learning reveals that electrochemical activity is governed by the complex interplay of chemical and structural factors. Out of 2394 measured compositions, a new highly active composition for the oxygen reduction reaction around Ru17Rh5Pd19Ir29Pt30 was identified.

cond-mat.mtrl-sci↗

arXiv2021

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 systems. This study offers insight into the number of experiments needed for exploring the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

physics.chem-ph↗

arXiv2020

Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation

Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datasets are then input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru for the oxygen reduction reaction. The method can identify optimal complex solid solutions for electrochemical reactions in an unprecedented manner.

cond-mat.mtrl-sci↗