arXiv subjects

Jan Rossmeisl

Publications and source records attributed to Jan Rossmeisl.

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

From Deposition Stress to Surface Reactivity: Strain-Dependent Hydrogen Evolution on Sputtered Platinum Thin Films

Strain has emerged as a promising approach for tuning electrocatalytic properties, yet its role in sputter-deposited thin films remains poorly understood. In this work, magnetron-sputtered platinum (Pt) thin films with different stress states were prepared by varying the sputter pressure. The resulting changes in microstructure, residual strain, and hydrogen evolution reaction (HER) activity were investigated using complementary characterization techniques and density functional theory (DFT) calculations. Structural analysis reveals a transition of (111)-textured Pt thin films from dense and smooth films at low pressures, to more porous microstructures with increased roughness at higher pressures. Electrochemical measurements show that films deposited at low sputter pressure exhibit the highest HER activity, while higher sputter pressures lead to reduced activity despite increased surface area. DFT calculations demonstrate that lattice strain alters hydrogen adsorption energetics and surface coverage on Pt(111), providing a mechanistic explanation for the observed activity trends. Overall, the results highlight that HER activity in sputtered Pt thin films is governed by the interplay of residual strain, microstructure, and hydrogen coverage.

cond-mat.mtrl-sci↗

arXiv2024

Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials

Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates. They present bottlenecks to the conventional use of density functional theory calculations, and consequently, the use of machine-learned potentials is becoming increasingly prevalent in atomic structure simulations. In this communication, we show the results of adjusting and fine-tuning the pretrained EquiformerV2 model from the Open Catalyst Project to infer adsorption energies of *OH and *O on the out-of-domain high-entropy alloy Ag-Ir-Pd-Pt-Ru. By applying an energy filter based on the local environment of the binding site the zero-shot inference is markedly improved and through few-shot fine-tuning the model yields state-of-the-art accuracy. It is also found that EquiformerV2, assuming the role of general machine learning potential, is able to inform a smaller, more focused direct inference model. This knowledge distillation setup boosts performance on complex binding sites. Collectively, this shows that foundational knowledge learned from ordered intermetallic structures, can be extrapolated to the highly disordered structures of solid-solutions. With the vastly accelerated computational throughput of these models, hitherto infeasible research in the high-entropy material space is now readily accessible.

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↗

arXiv2017

On the thickness of the double layer in ionic liquids

In this study, we examined the thickness of the electrical double layer (EDL) in ionic liquids using density functional theory (DFT) calculations and molecular dynamics (MD) simulations. We focused on the BF4- anion adsorption from 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF4) ionic liquid on the Au(111) surface. At both DFT and MD levels, we evaluated the capacitance-potential dependence for the Helmholtz model of the interface. Using MD simulations, we also explored a more realistic, multilayer EDL model accounting for the ion layering. Concurrent analysis of the DFT and MD results provides a ground for thinking whether the electrical double layer in ionic liquids is one- or multi-ionic-layer thick.

cond-mat.soft↗