DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

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 β12 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.

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...

pristine forms. Therefore, this finding is most critical for future studies where physical processes on calcite(104)−(2 × 1) pg can be influenced by the surface microscopic structure. ■ ASSOCIATED CONTENT * sı Supporting InformationThe Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpclett.2c03243.Geometrical structure of calcite( 104)−(2 × 1) (CIF) Materials and Methods: Sample preparation, STM and NC-AFM experiments, description of the algorithmic symmetry test, DFT calculations, and PPM image calculations including illustration of the two-atom probe particle model (Figure S1); extended details for the algorithmic symmetry test performed in Figure 1 (Figures S2) and examples of applying the symmetry test to data acquired with sharp (Figure S3) and unknown (Figure S4) tips; further experimental and theoretical results describing the CO adsorption: identification of two species (Figure S5), NC-AFM imaging of CO dimers (Figure S6), slice data acquired along the [421̅ ] direction (Figure S7), as well as DFT models for CO types I/I g (Figure S8) and II/II g (Figure S9); parameter analysis for PPM calculations (Figure S10

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 ϵ u is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level.