A Practical Guide to Surface Kinetic Monte Carlo Simulations

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g. fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.Comment: This document is the final Author's version of a manuscript that has been peer reviewed and accepted for publication in Frontiers in Chemistry. To access the final edited and published work see https://www.frontiersin.org/articles/10.3389/fchem.2019.00202/abstrac

Ligestilling, ægteskab og religion

In the 1880’s the Danish Women’s Movement put the majority rights of married women on the agenda. The female position  according to the Bible as well as the equality of the spouses became important issues in the debate. This article analyses the institution of marriage from 1912, when the marriage ritual was  reformed, until the new marriage laws in 1922 and 1925.  Interestingly enough there was no conflict between religion and equality, although religion in general was considered to  legitimate female subordination in society. These two reforms - the marriage ritual and the marriage laws - modernised the institution of marriage and contributed to women’s political subjectivity and prepared women for the welfare system based on universal and individual rights

Structure and stability of small H clusters on graphene

The structure and stability of small hydrogen clusters adsorbed on graphene is studied by means of Density Functional Theory (DFT) calculations. Clusters containing up to six H atoms are investigated systematically -- the clusters having either all H atoms on one side of the graphene sheet (\textit{cis}-clusters) or having the H atoms on both sides in an alternating manner (\textit{trans}-cluster). The most stable cis-clusters found have H atoms in ortho- and para-positions with respect to each other (two H's on neighboring or diagonally opposite carbon positions within one carbon hexagon) while the most stable trans-clusters found have H atoms in ortho-trans-positions with respect to each other (two H's on neighboring carbon positions, but on opposite sides of the graphene). Very stable trans-clusters with 13-22 H atoms were identified by optimizing the number of H atoms in ortho-trans-positions and thereby the number of closed, H-covered carbon hexagons. For the cis-clusters, the associative H$_2$ desorption was investigated. Generally, the desorption with the lowest activation energy proceeds via para-cis-dimer states, i.e.\ involving somewhere in the H clusters two H atoms that are positioned on opposite sites within one carbon hexagon. H$_2$ desorption from clusters lacking such H pairs is calculated to occur via hydrogen diffusion causing the formation of para-cis-dimer states. Studying the diffusion events showed a strong dependence of the diffusion energy barriers on the reaction energies and a general odd-even dependence on the number of H atoms in the cis-clusters.Comment: 11 pages, 11 figures, to appear in Phys. Rev.

Ab initio thermodynamics of hydrocarbons relevant to graphene growth at solid and liquid Cu surfaces

Using ab initio thermodynamics, the stability of a wide range of hydrocarbon adsorbates under various chemical vapor deposition (CVD) conditions (temperature, methane and hydrogen pressures) used in experimental graphene growth protocols at solid and liquid Cu surfaces has been explored. At the employed high growth temperatures around the melting point of Cu, we find that commonly used thermodynamic models such as the harmonic oscillator model may no longer be accurate. Instead, we account for the translational and rotational mobility of adsorbates using a recently developed hindered translator and rotator model or a two-dimensional ideal gas model. The thermodynamic considerations turn out to be crucial for explaining experimental results and allow us to improve and extend the findings of earlier theoretical studies regarding the role of hydrogen and hydrocarbon species in CVD. In particular, we find that smaller hydrocarbons will completely dehydrogenate under most CVD conditions. For larger clusters our results show that metal-terminated and hydrogen-terminated edges have very similar stabilities. While both cluster types might thus form during the experiment, we show that the low binding strength of clusters with hydrogen-terminated edges could result in instability towards desorption

Predicting binding energies of astrochemically relevant molecules via machine learning

The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive, but inaccurate, linear addition method. In this work we propose a new method based on machine learning for predicting binding energies that is accurate, yet computationally inexpensive. A machine learning model based on Gaussian Process Regression is created and trained on a database of binding energies of molecules collected from laboratory experiments presented in the literature. The molecules in the database are categorized by their features, such as mono- or multilayer coverage, binding surface, functional groups, valence electrons, and H-bond acceptors and donors. The performance of the model is assessed with five-fold and leave-one-molecule-out cross validation. Predictions are generally accurate, with differences between predicted and literature binding energies values of less than $\pm$20\%. The validated model is used to predict the binding energies of twenty one molecules that have recently been detected in the interstellar medium, but for which binding energy values are not known. A simplified model is used to visualize where the snowlines of these molecules would be located in a protoplanetary disk. This work demonstrates that machine learning can be employed to accurately and rapidly predict binding energies of molecules. Machine learning complements current laboratory experiments and quantum chemical computational studies. The predicted binding energies will find use in the modelling of astrochemical and planet-forming environments.Comment: Accepted in astronomy and astrophysic