Interpretability of high-resolution transmission electron microscopy images.

High-resolution electron microscopy is a well-suited tool for characterizing the nanoscale structure of materials. However, the interaction of the sample and the high-energy electrons of the beam can often have a detrimental impact on the sample structure. This effect can only be alleviated by decreasing the number of electrons to which the sample is exposed but will come at the cost of a decreased signal-to-noise ratio in the resulting image. Images with low signal to noise ratios are often challenging to interpret as parts of the sample with a low interaction with the electron beam are reproduced with very low contrast. Here we suggest simple measures as alternatives to the conventional signal-to-noise ratio and investigate how these can be used to predict the interpretability of the electron microscopy images. We test the models on a sample consisting of gold nanoparticles supported on a cerium dioxide substrate. The models are evaluated based on series of images acquired at varying electron dose.

GPAW: An open Python package for electronic structure calculations.

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials.

We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and equivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity.

Quantifying noise limitations of neural network segmentations in high-resolution transmission electron microscopy.

Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e.e-/Å2) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20-30 e-/Å2, and converges at 200 e-/Å2 where a full segmentation of the nanoparticle is achieved. Between 30 and 200 e-/Å2 object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented.

Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning.

Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.

Structure and energetics of liquid water-hydroxyl layers on Pt(111).

The interactions between liquid water and hydroxyl species on Pt(111) surfaces have been intensely investigated due to their importance to fuel cell electrocatalysis. Here we present a molecular dynamics study of their structure and energetics using an ensemble of neural network potentials, which allow us to obtain unprecedented statistical sampling. We first study the energetics of hydroxyl formation, where we find a near-linear adsorption energy profile, which exhibits a soft and gradual increase in the differential adsorption energy at high hydroxyl coverages. This is strikingly different from the predictions of the conventional bilayer model, which displays a kink at 1/3ML OH coverage indicating a sizeable jump in differential adsorption energy, but within the statistical uncertainty of previously reported ab initio molecular dynamics studies. We then analyze the structure of the interface, where we provide evidence for the water-OH/Pt(111) interface being hydrophobic at high hydroxyl coverages. We furthermore explain the observed adsorption energetics by analyzing the hydrogen bonding in the water-hydroxyl adlayers, where we argue that the increase in differential adsorption energy at high OH coverage can be explained by a reduction in the number of hydrogen bonds from the adsorbed water molecules to the hydroxyls.

Is the water/Pt(111) interface ordered at room temperature?

The structure of the water/Pt(111) interface has been a subject of debate over the past decades. Here, we report the results of a room temperature molecular dynamics study based on neural network potentials, which allow us to access long time scale simulations while retaining ab initio accuracy. We find that the water/Pt(111) interface is characterized by a double layer composed of a primary, strongly bound adsorption layer with a coverage of ∼0.15 ML, which is coupled to a secondary, weakly bound adsorption layer with a coverage of ∼0.58 ML. By studying the order of the primary adsorption layer, we find that there is an effective repulsion between the adsorbed water molecules, which gives rise to a dynamically changing, semi-ordered interfacial structure, where the water molecules in the primary adsorption layer are distributed homogeneously across the interface, forming frequent hydrogen bonds to water molecules in the secondary adsorption layer. We further show that these conclusions are beyond the time scales accessible to ab initio molecular dynamics.

Transformations of supported gold nanoparticles observed by in situ electron microscopy.

Oxide supported metal nanoparticles play an important role in heterogeneous catalysis. However, understanding the metal/oxide interface and their evolution under reaction conditions remains challenging. Herein, we investigate the interface between Au nanoparticles and a CeO2 substrate by environmental transmission electron microscopy with atomic resolution. We find that the Au nanoparticles have two preferential epitaxial relationships with the substrate, i.e. Type I (111)[-110]CeO2//(111)[-110]Au and Type II (111)[-110]CeO2//(111)[1-10]Au orientation relationships, where Type I is preferred. In situ observations in the presence of O2 show that the gas can stimulate the supported Au nanoparticles to transform between these two orientations even at room temperature. Moreover, when increasing the temperature to 973 K, the transformation of an Au nanoparticle between the two orientation states and a non-crystalline state in the presence of O2 is also observed. DFT calculations of the binding between Au and CeO2 in the two relationships are strongly influenced by the presence of oxygen vacancies. For a given position of a vacancy, there is a significant energy difference between the energy of the two types. However, for some positions, Type I is preferred, and for others, Type II, but the most favourable position of the vacancy for the two types has a very similar energy. This is consistent with the observation of both types of adhesion.

Rich Ground-State Chemical Ordering in Nanoparticles: Exact Solution of a Model for Ag-Au Clusters.

We show that nanoparticles can have very rich ground-state chemical order. This is illustrated by determining the chemical ordering of Ag-Au 309-atom Mackay icosahedral nanoparticles. The energy of the nanoparticles is described using a cluster expansion model, and a mixed integer programming approach is used to find the exact ground-state configurations for all stoichiometries. The chemical ordering varies widely between the different stoichiometries and displays a rich zoo of structures with nontrivial ordering.

The atomic simulation environment-a Python library for working with atoms.

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Correction to: Accuracy of surface strain measurements from transmission electron microscopy images of nanoparticles.

[This corrects the article DOI: 10.1186/s40679-017-0047-0.].

Tuning the activity of Pt alloy electrocatalysts by means of the lanthanide contraction.

The high platinum loadings required to compensate for the slow kinetics of the oxygen reduction reaction (ORR) impede the widespread uptake of low-temperature fuel cells in automotive vehicles. We have studied the ORR on eight platinum (Pt)-lanthanide and Pt-alkaline earth electrodes, Pt5M, where M is lanthanum, cerium, samarium, gadolinium, terbium, dysprosium, thulium, or calcium. The materials are among the most active polycrystalline Pt-based catalysts reported, presenting activity enhancement by a factor of 3 to 6 over Pt. The active phase consists of a Pt overlayer formed by acid leaching. The ORR activity versus the bulk lattice parameter follows a high peaked "volcano" relation. We demonstrate how the lanthanide contraction can be used to control strain effects and tune the activity, stability, and reactivity of these materials.

Correlation between diffusion barriers and alloying energy in binary alloys.

In this paper, we explore the notion that a negative alloying energy may act as a descriptor for long term stability of Pt-alloys as cathode catalysts in low temperature fuel cells. Using density functional theory calculations, we show that there is a correlation between the alloying energy of an alloy, and the diffusion barriers of the minority component. Alloys with a negative alloying energy may show improved long term stability, despite the fact that there is typically a greater thermodynamic driving force towards dissolution of the solute metal over alloying. In addition to Pt, we find that this trend also appears to hold for alloys based on Al and Pd.

Longitudinal domain wall formation in elongated assemblies of ferromagnetic nanoparticles.

Through evaporation of dense colloids of ferromagnetic ~13 nm ε-Co particles onto carbon substrates, anisotropic magnetic dipolar interactions can support formation of elongated particle structures with aggregate thicknesses of 100-400 nm and lengths of up to some hundred microns. Lorenz microscopy and electron holography reveal collective magnetic ordering in these structures. However, in contrast to continuous ferromagnetic thin films of comparable dimensions, domain walls appear preferentially as longitudinal, i.e., oriented parallel to the long axis of the nanoparticle assemblies. We explain this unusual domain structure as the result of dipolar interactions and shape anisotropy, in the absence of inter-particle exchange coupling.

Ab initio van der waals interactions in simulations of water alter structure from mainly tetrahedral to high-density-like.

The structure of liquid water at ambient conditions is studied in ab initio molecular dynamics simulations in the NVE ensemble using van der Waals (vdW) density-functional theory, i.e., using the new exchange-correlation functionals optPBE-vdW and vdW-DF2, where the latter has softer nonlocal correlation terms. Inclusion of the more isotropic vdW interactions counteracts highly directional hydrogen bonds, which are enhanced by standard functionals. This brings about a softening of the microscopic structure of water, as seen from the broadening of angular distribution functions and, in particular, from the much lower and broader first peak in the oxygen-oxygen pair-correlation function (PCF) and loss of structure in the outer hydration shells. Inclusion of vdW interactions is shown to shift the balance of resulting structures from open tetrahedral to more close-packed. The resulting O-O PCF shows some resemblance with experiment for high-density water (Soper, A. K. and Ricci, M. A. Phys. Rev. Lett. 2000, 84, 2881), but not directly with experiment for ambient water. Considering the accuracy of the new functionals for interaction energies, we investigate whether the simulation protocol could cause the deviation. An O-O PCF consisting of a linear combination of 70% from vdW-DF2 and 30% from low-density liquid water, as extrapolated from experiments, reproduces near-quantitatively the experimental O-O PCF for ambient water. This suggests the possibility that the new functionals may be reliable and that instead larger-scale simulations in the NPT ensemble, where the density is allowed to fluctuate in accordance with proposals for supercooled water, could resolve the apparent discrepancy with the measured PCF.

Memory effects in nonadiabatic molecular dynamics at metal surfaces.

We study the effect of temporal correlation in a Langevin equation describing nonadiabatic dynamics at metal surfaces. For a harmonic oscillator, the Langevin equation preserves the quantum dynamics exactly and it is demonstrated that memory effects are needed in order to conserve the ground state energy of the oscillator. We then compare the result of Langevin dynamics in a harmonic potential with a perturbative master equation approach and show that the Langevin equation gives a better description in the nonperturbative range of high temperatures and large friction. Unlike the master equation, this approach is readily extended to anharmonic potentials. Using density functional theory, we calculate representative Langevin trajectories for associative desorption of N(2) from Ru(0001) and find that memory effects lower the dissipation of energy. Finally, we propose an ab initio scheme to calculate the temporal correlation function and dynamical friction within density functional theory.

Quantum corrected Langevin dynamics for adsorbates on metal surfaces interacting with hot electrons.

We investigate the importance of including quantized initial conditions in Langevin dynamics for adsorbates interacting with a thermal reservoir of electrons. For quadratic potentials the time evolution is exactly described by a classical Langevin equation and it is shown how to rigorously obtain quantum mechanical probabilities from the classical phase space distributions resulting from the dynamics. At short time scales, classical and quasiclassical initial conditions lead to wrong results and only correctly quantized initial conditions give a close agreement with an inherently quantum mechanical master equation approach. With CO on Cu(100) as an example, we demonstrate the effect for a system with ab initio frictional tensor and potential energy surfaces and show that quantizing the initial conditions can have a large impact on both the desorption probability and the distribution of molecular vibrational states.

Electrochemical control of quantum interference in anthraquinone-based molecular switches.

Using first-principles calculations we analyze the electronic transport properties of a recently proposed anthraquinone-based electrochemical switch. Robust conductance on/off ratios of several orders of magnitude are observed due to destructive quantum interference present in the anthraquinone but absent in the hydroquinone molecular bridge. A simple explanation of the interference effect is achieved by transforming the frontier molecular orbitals into localized molecular orbitals thereby obtaining a minimal tight-binding model describing the transport in the relevant energy range in terms of hopping via the localized orbitals. The topology of the tight-binding model, which is dictated by the symmetries of the molecular orbitals, determines the amount of quantum interference.