Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases

Are they the adventurers? Comparing the risk attitudes of internationally mobile and non-mobile Germans

Moving–particularly to a new country–is fraught with risks as migrants leave familiar legal frameworks and cultural institutions behind them. To date, little is known about the psychological determinants of international migration. This chapter helps to fill this gap by analysing data from the first wave of the German Emigration and Remigration Panel Study (GERPS) in combination with data on non-mobile individuals from the German Socio-Economic Panel Study (SOEP). The analyses presented examine whether the risk attitudes of internationally mobile Germans (‘movers’) differ from those of their non-mobile counterparts (‘stayers’). The results show that–with control for key socio-demographic and socio-economic determinants of risk affinity–both emigrants and remigrants report a significantly higher willingness to take risks than stayers. Risk affinity differs within the group of internationally mobile individuals: Emigrants moving to geographically and culturally distant non-European countries report higher risk affinity than those moving to Germany’s neighbouring countries. Emigrants with multiple previous emigration periods are also more willing to take risks. These findings suggest that voluntary emigration from wealthy countries like Germany is only partly a matter of living conditions. Rather, (repeated) emigration seems to be a matter of personality and an expression of a more adventurous lifestyle

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases

Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory

Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in particular, for understanding the processes occurring in the interior of icy planets, such as Uranus and Neptune, where small hydrocarbons are subjected to high temperature and pressure. We propose a computationally cheap methodology based on an originally developed ten-reaction model, and the configurational model from random graph theory. This methodology yields to accurate predictions for molecule size distributions for a variety of initial chemical compositions and temperatures ranging from 3200K to 5000K. Specifically, we show that the size distribution of small molecules is particularly well predicted, and the size of the largest molecule can be accurately predicted provided that it is not too large.Comment: 15 pages, 10 figures + supplementary (2 pages, 2 figures). Submitted to the Journal of Physical Chemistry

The art of survey question formulation

enn du etwas wissen willst, frage! – Was im Alltag so einfach klingt, ist in der Methodenforschung ein aufwendiger Prozess. Sowohl das Formulieren von Fragen als auch die Gestaltung des Fragebogens bedarf der Berücksichtigung unterschiedlicher Regeln, ohne die durch Befragungen gewonnene Daten schnell unbrauchbar werden können. So spielen in einem systematischen Frage-Antwort-Vorgang zentrale Kommunikationsregeln und psychologische Abläufe eine Rolle, die es als Forscher dringend zu beachten gilt. Aufbauend auf diesen zentralen Erkenntnissen befasst sich der folgende Beitrag mit unterschiedlichen Aspekten, die die Qualität von Fragebögen beeinflussen. Anhand von praxisbezogenen Beispielen, die aus Erfahrungen mit diversen Befragungen sowie einer systematischen Methodenforschung resultieren, werden dabei dem Leser Leitlinien zur Optimierung von eigenen Fragebögen an die Hand gegeben. Gleichwohl gilt es dabei zu beachten, dass es allgemeingültige Regeln, die auf jede Forschungsfrage, Untersuchungspopulation oder auch Erhebungsart anwendbar sind, nicht geben kann. Die hier erarbeiteten Instruktionen dienen vielmehr der Vermittlung wesentlicher Kenntnisse, die zum Vermeiden grober Fehler führen sollen und es dem Forscher zugleich erlauben, bei Bedarf die Leitlinien nach kritischer Betrachtung eigenständig anzupassen