Improved description of metal oxide stability:Beyond the random phase approximation with renormalized kernels

The renormalized adiabatic PBE (rAPBE) method has recently been shown to comprise a significant improvement over the random phase approximation (RPA) for total energy calculations of simple solids and molecules. Here we consider the formation energies of 19 group I and II metal oxides and a few transition-metal oxides. The mean absolute error relative to experiments is 0.21 eV and 0.38 eV per oxygen atom for rAPBE and RPA, respectively, and thus the rAPBE method greatly improves the description of metal-oxygen bonds across a wide range of oxides. The failure of the RPA can be partly attributed to the lack of error cancellation between the correlation energy of the oxide on the one hand and the bulk metal and oxygen molecule on the other hand, which are all separately predicted much too negative by the RPA. We ascribe the improved performance of the rAPBE to its significantly better description of absolute correlation energies which reduces the need for error cancellation. The rAPBE is just one out of an entire class of renormalized exchange-correlation kernels which should be further investigated.The authors acknowledge support from the Danish Council for Independent Research’s Sapere Aude Program, Grant No. 11-1051390. The Center for Nanostructured Graphene is sponsored by the Danish National Research Foundation, Project DNRF58. T.B. thanks DOE-BES for funding to SUNCAT.Peer reviewe

A supply-side story of oil and gas: how fear of the future dictates behaviour today

The aim of this dissertation is to determine the spatial and dynamic mechanisms that govern the supply of oil and natural gas. Specifically, the research evaluates how fear of the future affects behavior today and thereby it tests whether non-renewable resource owners behave in the forward-looking manner described by Harold Hotelling in the 1930s. Understanding what governs the supply of oil and natural gas is vital, as these fuels have significant economic and environmental implications for the planet. Integrating original research papers, the dissertation unfolds in seven chapters. The first and second chapters provide the foundation for the following research, by introducing the existing literature on oil and gas management. The subsequent three chapters discuss common pool problems as a method of identifying forward-looking behavior. Retaining this focus on weak property rights, chapter six evaluates the short-term relationship between government stability and oil extraction in authoritarian petro-states. The final chapter summarizes the main findings and outlines key implications. Drawing on new datasets and novel methodological tools, this dissertation demonstrates how fear of common pool problems governs exploration and extraction in the oil and gas industry today. However, contrary to conventional theory, this dissertation does not find that political instability motivates authoritarian regimes to accelerate their extraction

Machine-learning enabled optimization of atomic structures using atoms with fractional existence

We introduce a method for global optimization of the structure of atomic systems that uses additional atoms with fractional existence. The method allows for movement of atoms over long distances bypassing energy barriers encountered in the conventional position space. The method is based on Gaussian processes, where the extrapolation to fractional existence is performed with a vectorial fingerprint. The method is applied to clusters and two-dimensional systems, where the fractional existence variables are optimized while keeping the atomic positions fixed on a lattice. Simultaneous optimization of atomic coordinates and existence variables is demonstrated on copper clusters of varying size. The existence variables are shown to speed up the global optimization of large and particularly difficult-to-optimize clusters.Comment: 6 pages, 5 figures, plus supplemen