Correction: Water adsorption lifts the (2 × 1) reconstruction of calcite(104).

Correction for 'Water adsorption lifts the (2 × 1) reconstruction of calcite(104)' by Jonas Heggemann et al., Phys. Chem. Chem. Phys., 2024, 26, 21365-21369, https://doi.org/10.1039/D3CP01408H.

Water adsorption lifts the (2 × 1) reconstruction of calcite(104).

The adsorption of water on calcite(104) is investigated in ultra-high vacuum by density functional theory (DFT) and non-contact atomic force microscopy (NC-AFM) in the coverage regime of up to one monolayer (ML). DFT calculations reveal a clear preference for water to adsorb on the bulk-like carbonate group rows of the (2 × 1) reconstructed surface. Additionally, an apparent water attraction due to carbonate group reorientation suggest island formation for water adsorbed on the reconstructed carbonate group rows. Experimentally, water is found to exclusively occupy specific positions within the (2 × 1) unit cell up to 0.5 ML, to form islands at coverage between 0.5 and 1 ML, and to express a (1 × 1) structure at coverage of a full monolayer.

Differences in Molecular Adsorption Emanating from the (2 × 1) Reconstruction of Calcite(104).

Calcite, in the natural environment the most stable polymorph of calcium carbonate (CaCO3), not only is an abundant mineral in the Earth's crust but also forms a central constituent in the biominerals of living organisms. Intensive studies of calcite(104), the surface supporting virtually all processes, have been performed, and the interaction with a plethora of adsorbed species has been studied. Surprisingly, there is still serious ambiguity regarding the properties of the calcite(104) surface: effects such as a row-pairing or a (2 × 1) reconstruction have been reported, yet so far without physicochemical explanation. Here, we unravel the microscopic geometry of calcite(104) using high-resolution atomic force microscopy (AFM) data acquired at 5 K combined with density functional theory (DFT) and AFM image calculations. A (2 × 1) reconstruction of a pg-symmetric surface is found to be the thermodynamically most stable form. Most importantly, a decisive impact of the (2 × 1) reconstruction on adsorbed species is revealed for carbon monoxide.

Predicting hydration layers on surfaces using deep learning.

Characterisation of the nanoscale interface formed between minerals and water is essential to the understanding of natural processes, such as biomineralization, and to develop new technologies where function is dominated by the mineral-water interface. Atomic force microscopy offers the potential to characterize solid-liquid interfaces in high-resolution, with several experimental and theoretical studies offering molecular scale resolution by linking measurements directly to water density on the surface. However, the theoretical techniques used to interpret such results are computationally intensive and development of the approach has been limited by interpretation challenges. In this work, we develop a deep learning architecture to learn the solid-liquid interface of polymorphs of calcium carbonate, allowing for the rapid predictions of density profiles with reasonable accuracy.

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the d-band Hilbert-transform ϵu is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.

Borophene layers on an Al(111) surface - the finding of a borophene layer with hexagonal double chains and B9 nonagons using ab initio calculations.

We studied the stability of several borophene layers on an Al(111) surface and found a structure called 9R using ab initio calculations. This layer competes with χ3 and ß12 borophene layers and is made up of boron nonagons that form a network of hexagonal boron double chains. Remarkably, it has no B6 hexagon unlike other borophene layers. All three layers lie significantly lower in energy than the honeycomb layer recently reported on the Al(111) surface [W. Li, et al., Sci. Bull., 2018, 63, 282]. We discuss the structural stability and electronic structures of different borophene layers in light of the role of the filling factor f of boron atoms in boron hexagons in a honeycomb layer as well as charge transfer from the Al substrate to the borophene layer as obtained from the Bader charge analysis. The electron localization function shows that the 9R layer has two-center bonding within the nonagon rings and three-center bonding between the rings. Calculations of the phonon spectra show that a free 9R layer is dynamically stable raising the hope of its isolation. The electronic structure shows that in all cases the borophene layer is metallic.