publications
2024
- A Comparison of Modern Solvation Models for Oxygen Reduction at the Pt(111) InterfaceTom Demeyere, and Chris-Kriton SkylarisThe Journal of Physical Chemistry C, Nov 2024
Solvation effects play an important role in the thermodynamics of catalytic reactions; however, current implicit solvation models often fail to accurately capture specific local effects, such as hydrogen bonding, limiting their ability to systematically incorporate solvation effects into quantum mechanical simulations. In this study, we investigate the Reference Interaction Site Model (RISM) and apply it to the platinum(111) interface, using the Oxygen Reduction Reaction as a case study. We compare RISM to the charge-asymmetric nonlocally determined local-electron (CANDLE) solvation model, which belongs to the class of Poisson–Boltzmann models. Our results demonstrate that RISM, with the appropriately parametrized water model can accurately describe properties of the solvated Pt(111) surface such as solvation free energies, workfunctions, and capacitances and capture subtle effects due to electrolyte concentration and explicit adsorbates. We find that including lone pairs in the water model proves to be crucial for obtaining accurate results, highlighting the importance of water nonbonding orbitals in solvation effects at the Pt(111) interface. Furthermore, RISM enables the computation of previously inaccessible properties, such as the solvent/electrolyte density near charged electrodes, providing valuable insights into the electrochemical double layer structure. Our findings suggest that RISM could serve as a computationally efficient alternative for studying electrode–electrolyte interfaces, paving the way for systematic incorporation of solvation effects into computational studies.
- Multi-Scale Modeling and Experimental Investigation of Oxidation Behavior in Platinum NanoparticlesTom Demeyere, Husn-Ubayda Islam, Tom Ellaby, and 3 more authorsOct 2024
Understanding the oxidation behavior of Pt nanoparticles (NPs) is crucial for developing durable and efficient catalysts. In this study, we investigate the oxidation of a realistic Pt NP, retrieved from scanning transmission electron microscopy (STEM) images. We use a multistep approach combining ReaxFF and MACE-MP-0 forcefields with Density Functional Theory (DFT) calculations. Our Monte Carlo simulations reveal high oxidation of the nanoparticle, with oxygen penetrating deep into the core. We explore the plausibility of these configurations by carrying out XRD, TEM and EXAFS measurements on samples of various average particle sizes. Progressing in our workflow, we find that 100 ns of thermostated dynamics at 350 K using the ReaxFF forcefield leads to the formation of detached Pt6O8 species. To explore the validity of this small platinum-oxide cluster, we first optimize the geometries using the recent MACE-MP-0 forcefield resulting in structures without the species. We then compare both forcefields to DFT calculations showing closer agreement for MACE-MP-0 compared to ReaxFF. Finally, we discuss the electronic structure of our oxidized nanoparticles spanning a whole range of oxygen coverages, finding substantial changes in the Pt-5d and O-2p projected density of states of the platinum structure as the coverage increases. Our findings emphasize the importance of accurately describing the potential energy surface and explicitly modeling oxygen coverage to predict catalytically relevant properties at high potentials. This study aims to provide a foundation for understanding the complex interplay between nanoparticle structure, oxidation state, and catalytic performance, aiming to guide the rational design of advanced catalytic materials.
2023
- A Workflow for Identifying Viable Crystal Structures with Partially Occupied Sites Applied to the Solid Electrolyte Cubic Li7La3Zr2O12Julian Holland, Tom Demeyere, Arihant Bhandari, and 3 more authorsThe Journal of Physical Chemistry Letters, Nov 2023
To date, experimental and theoretical works have been unable to uncover the ground-state configuration of the solid electrolyte cubic Li7La3Zr2O12 (c-LLZO). Computational studies rely on an initial low-energy structure as a reference point. Here, we present a methodology for identifying energetically favorable configurations of c-LLZO for a crystallographically predicted structure. We begin by eliminating structures that involve overlapping Li atoms based on nearest neighbor counts. We further reduce the configuration space by eliminating symmetry images from all remaining structures. Then, we perform a machine learning-based energetic ordering of all remaining structures. By considering the geometrical constraints that emerge from this methodology, we determine that a large portion of previously reported structures may not be feasible or stable. The method developed here could be extended to other ion conductors. We provide a database containing all of the generated structures with the aim of improving accuracy and reproducibility in future c-LLZO research.