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While offering high resolution atomic and electronic structure, scanning probe microscopy techniques have found greater challenges in providing reliable electrostatic characterization on the same scale. In this work, we offer electrostatic discovery atomic force microscopy, a machine learning based method which provides immediate maps of the electrostatic potential directly from atomic force microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach offers reliable atomic scale electrostatic maps on any system with minimal computational overhead.

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.

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.

Friction and wear are the source of every mechanical device failure, and lubricants are essential for the operation of the devices. These physical phenomena have a complex nature so that no model capable of accurately predicting the behavior of lubricants exists. Thus, lubricants cannot be designed from scratch but have to be screened through expensive trial-error tests. In this study we propose a machine learning (ML) method that infers the relationship between chemical composition of lubricants and their performance from a database. Because no such database of desirable size and completeness is publicly available, we compiled one from molecular dynamics (MD) simulations of toy-model fluids nanoconfined between shearing surfaces. The fluid-friction relation is modeled by a Bayesian neural network (BNN), trained to reproduce the results for a training set of fluids. Due to the inhomogeneous data distribution it was necessary to carefully pick fluids for training and validation from the database with advanced clustering algorithms, rather than using the standard random selection. Different BNNs were then trained on the data clusters and their predictions combined into a mixture of experts. The model provides a prediction of lubricants performance as well as an error bar, at a fraction of the cost of MD. Because most values agree with the actual MD simulations within the estimated error σ, we conclude that the model is satisfactory. This method addresses the challenges brought by noisy, badly distributed, high-dimensional data that are likely to appear in reality as well, and it can be extended to real fluids, if a database could be provided.

Frequency modulation atomic force microscopy (FM-AFM) experiments were performed on the calcite (10[Formula: see text]4) surface in pure water, and a detailed analysis was made of the 2D images at a variety of frequency setpoints. We observed eight different contrast patterns that reproducibly appeared in different experiments and with different measurement parameters. We then performed systematic free energy calculations of the same system using atomistic molecular dynamics to obtain an effective force field for the tip-surface interaction. By using this force field in a virtual AFM simulation we found that each experimental contrast could be reproduced in our simulations by changing the setpoint, regardless of the experimental parameters. This approach offers a generic method for understanding the wide variety of contrast patterns seen on the calcite surface in water, and is generally applicable to AFM imaging in liquids.

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.

We report the observation of kinesin driven quantum dots (QDs) trapped in a microtubule loop, allowing the investigation of moving QDs for a long time and an unprecedented long distance. The QD conjugates did not depart from our observational field of view, enabling the tracking of specific conjugates for more than 5 min. The unusually long run length and the periodicity caused by the loop track allow comparing and studying the trajectory of the kinesin driven QDs for more than 2 full laps, i.e., about 70 μm, enabling a statistical analysis of interactions of the same kinesin driven object with the same obstacle. The trajectories were extracted and analyzed from kymographs with a newly developed algorithm. Despite dispersion, several repetitive trajectory patterns can be identified. A method evaluating the similarity is introduced allowing a quantitative comparison between the trajectories. The velocity variations appear strongly correlated to the presence of obstacles. We discuss the reasons making this long continuous travel distances on the loop track possible.

Atomic manipulation enables us to fabricate a unique structure at the atomic scale. So far, many atomic manipulations have been reported on conductive surfaces, mainly at low temperature with scanning tunnelling microscopy, but atomic manipulation on an insulator at room temperature is still a long-standing challenge. Here we present a systematic atomic manipulation on an insulating surface by advanced atomic force microscopy, enabling construction of complex patterns such as a 'Swiss cross' of substitutional bromine ions in the sodium chloride surface.

We used molecular dynamics simulations to study the structure and shear dynamics of two ionic liquids (ILs) featuring the same cation 1-butyl-3-methyl-imidazolium or [BMIM], paired with bis(trifluoromethanesulphonyl)amide [NTF2] and tetrafluoroborate [BF4] anions, confined between two hydroxylated silica surfaces. The results demonstrate how the shape of IL molecules affects their layering structure at hydroxylated silica surfaces and how the layered structure of nanoconfined liquids determines their dynamical properties at the molecular level. When [BMIM][NTF2] is sheared, larger molecular fluctuations in the inner layers are required to stabilise the system, and the resulting dynamics is irregular. The alternating charged layers in [BMIM][BF4] allow the system to stabilise through smaller oscillations, and the layers appear to shear on top of each other in a laminar fashion. The simulated dynamics explains qualitatively the relative change in viscosity that the two ILs exhibit when confined, as has been observed in previous experiments.

We provide unambiguous evidence that the applied electrostatic field displaces step atoms of ionic crystal surfaces by subpicometers in different directions via the measurement of the lateral force interactions by bimodal dynamic force microscopy combined with multiscale theoretical simulations. Such a small imbalance in the electrostatic interaction of the shifted anion-cation ions leads to an extraordinary long-range feature potential variation and is now detectable with the extreme sensitivity of the bimodal detection.

From an interplay of atom-resolved noncontact atomic force microscopy, surface x-ray diffraction experiments, and density functional theory calculations, we reveal the detailed atomic-scale structure of the (100) surface of an insulating ternary metal oxide, MgAl2O4 (spinel). We surprisingly find that the MgAl2O4(100) surface is terminated by an Al and O-rich structure with a thermodynamically favored amount of Al atoms interchanged with Mg. This finding implies that so-called Mg-Al antisites, which are defects in the bulk of MgAl2O4, become a thermodynamically stable and integral part of the surface.