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 produce precise chiral-edge graphene nanoribbons on Cu{111} using self-assembly and surface-directed chemical reactions. We show that, using specific properties of the substrate, we can change the edge conformation of the nanoribbons, segregate their adsorption chiralities, and restrict their growth directions at low surface coverage. By elucidating the molecular-assembly mechanism, we demonstrate that our method constitutes an alternative bottom-up strategy toward synthesizing defect-free zigzag-edge graphene nanoribbons.

Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper-gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper-gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

On-surface synthesis with molecular precursors has emerged as the de facto route to atomically well-defined graphene nanoribbons (GNRs) with controlled zigzag and armchair edges. On Au(111) and Ag(111) surfaces, the prototypical precursor 10,10′-dibromo-9,9′-bianthryl (DBBA) polymerizes through an Ullmann reaction to form straight GNRs with armchair edges. However, on Cu(111), irrespective of the bianthryl precursor (dibromo-, dichloro-, or halogen-free bianthryl), the Ullmann route is inactive, and instead, identical chiral GNRs are formed. Using atomically resolved noncontact atomic force microscopy (nc-AFM), we studied the growth mechanism in detail. In contrast to the nonplanar BA-derived precursors, planar dibromoperylene (DBP) molecules do form armchair GNRs by Ullmann coupling on Cu(111), as they do on Au(111). These results highlight the role of the substrate, precursor shape, and molecule–molecule interactions as decisive factors in determining the reaction pathway. Our findings establish a new design paradigm for molecular precursors and opens a route to the realization of previously unattainable covalently bonded nanostructures.

We use self-assembly to fabricate and to connect precise graphene nanoribbons end to end. Combining scanning tunneling microscopy, Raman spectroscopy, and density functional theory, we characterize the chemical and electronic aspects of the interconnections between ribbons. We demonstrate how the substrate effects of our self-assembly can be exploited to fabricate graphene structures connected to desired electrodes.

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules, due to difficulties with interpretation of highly distorted AFM images originating from non-planar molecules. Here we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to apply high-resolution AFM to a large variety of systems for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.CO-AFM now offers an unprecedented window into molecular structure on surfaces -aside from the detailed resolution of the results of molecular assembly 11,12 , it is possible to study bond order 13 , charge distributions 14,15 and the individual steps of on-surface chemical reactions 16,17,18,19 .As yet, most CO-AFM studies have been focused on planar molecular systems, where the experimental image requires almost no interpretation 10,5,20 . Even where understanding is not immediately obvious, such as due to controversies over the nature of observed bonds 21 , efficient models have been developed 22,12,23,24,25 that explain the contrast mechanism in terms of the tip-surface interaction and CO lateral flexibility. However, the further the systems studied are from two-dimensional molecules containing only hydrogen and carbon, the more complex and time consuming (if not impossible) the interpretation process becomes 17,26,27,28,29 . While recent measurements using rigid O-terminated copper tips makes interpreting images of flat systems even easier 30,31 , the rigidity also means even less atoms can be characterized when moving to 3D systems. In recent years, CO-AFM has moved towards measuring truly unknown structures 29,32,33,34 , where it has overcome many of the limitations of techniques such as nuclear magnetic resonance and mass spectrometry. It is clear that this trend is going to continue, and potentially even accelerate, in particular for innovative studies, e.g. in life sciences or biochemistry 6,7 , demonstrated manifestly in the first CO-AFM images of DNA 35 . Reliable interpretation of such data becomes a vast exploration through all possible molecules, configurations and imaging parameters to find agreement. This is impractical in anything beyond very simple systems, severely limiting the ultimate power of the technique.In this work, we couple a systematic software approach with detailed experimental CO-AFM imaging to understand and predict AFM images for molecules of any size, configuration or orientation without prior knowledge of the system being studied. We use the late...

Van der Waals forces are among the weakest, yet most decisive interactions governing condensation and aggregation processes and the phase behaviour of atomic and molecular matter. Understanding the resulting structural motifs and patterns has become increasingly important in studies of the nanoscale regime. Here we measure the paradigmatic van der Waals interactions represented by the noble gas atom pairs Ar–Xe, Kr–Xe and Xe–Xe with a Xe-functionalized tip of an atomic force microscope at low temperature. Individual rare gas atoms were fixed at node sites of a surface-confined two-dimensional metal–organic framework. We found that the magnitude of the measured force increased with the atomic radius, yet detailed simulation by density functional theory revealed that the adsorption induced charge redistribution strengthened the van der Waals forces by a factor of up to two, thus demonstrating the limits of a purely atomic description of the interaction in these representative systems.

According to Hückel theory, an anti-aromatic molecule possessing (4n)π-electrons becomes unstable. Although the stabilization has been demonstrated by radialene-type structures-fusing aromatic rings to anti-aromatic rings-in solution, such molecules have never been studied at a single molecular level. Here, we synthesize a cyclobutadiene derivative, dibenzo[b,h]biphenylene, by an on-surface intramolecular reaction. With a combination of high-resolution atomic force microscopy and density functional theory calculations, we found that a radialene structure significantly reduces the anti-aromaticity of the cyclobutadiene core, extracting π-electrons, while the small four-membered cyclic structure keeps a high density of the total charge.