arXiv subjects

Jakob Schiøtz

Publications and source records attributed to Jakob Schiøtz.

At least 19 recordsLinked to original sources

arXiv2025

Visualizing phonon edge states on molybdenum disulphide

We employ Molecular Dynamics (MD) simulations to study atom vibrational amplitudes in carbon-supported Molybdenum Disulphide (MoS2) nanoparticles. Enhanced and correlated atom vibrational amplitudes are observed as the nanoparticle edges are approached from the bulk, consistent with recent experimental High-Resolution Transmission Electron Microscopy (HR-TEM) observations by Chen et al (Nature Communications 12, 5007 (2021). Analysis of phonon modes in finite systems explains the experimental observation by low-energy phonon modes confined at the nanoparticle edge, underscoring the need of full MD modeling for accurate HR-TEM image interpretation. Noticeably, we introduce a workflow for training Equivariant Neural Network-based machine learning potentials using limited Density Functional Theory (DFT) calculations. This approach effectively captures both covalent and van der Waals interactions, enabling accurate extrapolations of DFT calculations to larger systems with built-in error estimation.

cond-mat.mtrl-sci↗

arXiv2025

Defect complexes in CrSBr revealed through electron microscopy and deep learning

Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and ab-initio calculations. We apply a custom-developed machine learning work flow to detect, classify and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab-initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are furthermore predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.

cond-mat.mtrl-sci↗

arXiv2025

Two-dimensional materials as ideal substrates for molecular quantum emitters

The generation and manipulation of non-classical states of light is central to quantum technologies. Color centers in insulators have been extensively studied for single-photon generation, but organic molecules immobilized on substrates have gained attention due to their superior scalability, large oscillator strengths, and tunable emission frequency. Here, we use first-principles calculations to investigate the photoemission from organic molecules adsorbed on 2D materials. Focusing on terrylene on hexagonal boron nitride (hBN), we obtain zero phonon line (ZPL) energies and emission lineshapes in excellent agreement with experiments. Notably, antisite defects in hBN can immobilize the molecule without influencing its key emission features. We further show that the main effect of the 2D substrate is to introduce sharp sidebands near the ZPL as a fingerprint of hindered rotational, translational, or bending modes of the molecule. Our findings provide insight into how substrate interactions shape the optical properties of molecular systems for quantum applications.

cond-mat.mtrl-sci↗

arXiv2024

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 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 (BSE), 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 of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW.

cond-mat.mtrl-sci↗

arXiv2023

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 eqivariant 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.

cond-mat.mes-hall↗

arXiv2023

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. $e^-/A^{2}$) 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 $e^-/A^{2}$, and converges at 200 $e^-/A^{2}$ where a full segmentation of the nanoparticle is achieved. Between 30 and 200 $e^-/A^{2}$ 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.

cond-mat.mtrl-sci↗

arXiv2022

Reconstructing the exit wave 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 MoS$_2$ 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.

cond-mat.mtrl-sci↗

arXiv2021

Room temperature structure and energetics of water-hydroxyl layers on Pt(111)

The interactions between water and hydroxyl species on Pt(111) surfaces have been intensely investigated due to their importance to fuel cell electrocatalysis. Here we present a room temperature 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.

cond-mat.mtrl-sci↗

arXiv2018

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 (MIP) approach is used to find the exact ground state configurations for all stoichiometries. The chemical ordering varies widely between the different stoichiometries, and display a rich zoo of structures with non-trivial ordering.

cond-mat.mtrl-sci↗

arXiv2018

A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images

Recording atomic-resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time-consuming manual structural identification. We have developed a deep learning-based algorithm for recognition of the local structure in TEM images, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. We apply the method to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.

cond-mat.mtrl-sci↗

arXiv2016

Robust Structural Identification via Polyhedral Template Matching

Successful scientific applications of large-scale molecular dynamics often rely on automated methods for identifying the local crystalline structure of condensed phases. Many existing methods for structural identification, such as Common Neighbour Analysis, rely on interatomic distances (or thresholds thereof) to classify atomic structure. As a consequence they are sensitive to strain and thermal displacements, and preprocessing such as quenching or temporal averaging of the atomic positions is necessary to provide reliable identifications. We propose a new method, Polyhedral Template Matching (PTM), which classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. Common Neighbour Analysis in the presence of thermal fluctuations. We demonstrate that the method can reliably be used to identify structures even in simulations near the melting point, and that it can identify the most common ordered alloy structures as well. In addition, the method makes it easy to identify the local lattice orientation in polycrystalline samples, and to calculate the local strain tensor. An implementation is made available under a Free and Open Source Software license.

cond-mat.mtrl-sci↗

arXiv2011

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 using van der Waals (vdW) density-functional theory, i.e. using the new exchange-correlation functionals optPBE-vdW and vdW-DF2. 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), indicating loss of structure in the outer solvation shells. In combination with softer non-local correlation terms, as in the new parameterization of vdW-DF, 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 (A. K. Soper and M. A. Ricci, Phys. Rev. Lett., 84:2881, 2000), but not directly with experiment for ambient water. However, an O-O PCF consisting of a linear combination of 70% from vdW-DF2 and 30% from experiment on low-density liquid water reproduces near-quantitatively the experimental O-O PCF for ambient water, indicating consistency with a two-liquid model with fluctuations between high- and low-density regions.

cond-mat.mtrl-sci↗

arXiv2010

Memory effects in non-adiabatic molecular dynamics at metal surfaces

We study the effect of temporal correlation in a Langevin equation describing non-adiabatic 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 non-perturbative 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 lowers the dissipation of energy. Finally, we propose an ab-initio scheme to calculate the temporal correlation function and dynamical friction within density functional theory.

cond-mat.mtrl-sci↗

arXiv2010

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.

cond-mat.mes-hall↗

arXiv2010

Vibrationally Mediated Control of Single Electron Transmission in Weakly Coupled Molecule-Metal Junctions

We propose a mechanism which allows one to control the transmission of single electrons through a molecular junction. The principle utilizes the emergence of transmission sidebands when molecular vibrational modes are coupled to the electronic state mediating the transmission. We will show that if a molecule-metal junction is biased just below a molecular resonance one may induce the transmission of a single electron by externally exciting a vibrational mode of the molecule. The analysis is quite general but requires that the molecular orbital does not hybridize strongly with the metallic states. As an example we perform a density functional theory (DFT) analysis of a benzene molecule between two Au(111) contacts and show that exciting a particular vibrational mode can give rise to transmission of a single electron

cond-mat.mes-hall↗

arXiv2010

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.

cond-mat.mtrl-sci↗

arXiv2009

Origin of power laws for reactions at metal surfaces mediated by hot electrons

A wide range of experiments have established that certain chemical reactions at metal surfaces can be driven by multiple hot electron mediated excitations of adsorbates. A high transient density of hot electrons is obtained by means of femtosecond laser pulses and a characteristic feature of such experiments is the emergence of a power law dependence of the reaction yield on the laser fluence $Y\sim F^n$. We propose a model of multiple inelastic scattering by hot electrons, which reproduces this power law and the experimentally found exponents of several experiments. All parameters are calculated within Density Functional Theory and the Delta Self-Consistent Field method. With a simplified assumption, the power law becomes exact and we obtain a simple and very useful physical interpretation of the exponent $n$, which represents the number of adsorbate vibrational states participating in the reaction.

physics.chem-ph↗

arXiv2009

Hot electron mediated desorption rates calculated from excited state potential energy surfaces

We present a model for Desorption Induce by (Multiple) Electronic Transitions (DIET/DIMET) based on potential energy surfaces calculated with the Delta Self-Consistent Field extension of Density Functional Theory. We calculate potential energy surfaces of CO and NO molecules adsorbed on various transition metal surfaces, and show that classical nuclear dynamics does not suffice for propagation in the excited state. We present a simple Hamiltonian describing the system, with parameters obtained from the excited state potential energy surface, and show that this model can describe desorption dynamics in both the DIET and DIMET regime, and reproduce the power law behavior observed experimentally. We observe that the internal stretch degree of freedom in the molecules is crucial for the energy transfer between the hot electrons and the molecule when the coupling to the surface is strong.

physics.chem-ph↗