Stochastic dynamics simulations in a new generalized ensemble

We develop a formulation for molecular dynamics, Langevin, and hybrid Monte Carlo algorithms in the recently proposed generalized ensemble that is based on a physically motivated realisation of Tsallis weights. The effectiveness of the methods are tested with an energy function for a protein system. Simulations in this generalized ensemble by the three methods are performed for a penta peptide, Met-enkephalin. For each algorithm, it is shown that from only one simulation run one can not only find the global-minimum-energy conformation but also obtain probability distributions in canonical ensemble at any temperature, which allows the calculation of any thermodynamic quantity as a function of temperature.Comment: to appear in Chem. Phy. Let

Effect of annealing on the depth profile of hole concentration in (Ga,Mn)As

The effect of annealing at 250 C on the carrier depth profile, Mn distribution, electrical conductivity, and Curie temperature of (Ga,Mn)As layers with thicknesses > 200 nm, grown by molecular-beam epitaxy at low temperatures, is studied by a variety of analytical methods. The vertical gradient in hole concentration, revealed by electrochemical capacitance-voltage profiling, is shown to play a key role in the understanding of conductivity and magnetization data. The gradient, basically already present in as-grown samples, is strongly influenced by post-growth annealing. From secondary ion mass spectroscopy it can be concluded that, at least in thick layers, the change in carrier depth profile and thus in conductivity is not primarily due to out-diffusion of Mn interstitials during annealing. Two alternative possible models are discussed.Comment: 8 pages, 8 figures, to appear in Phys. Rev.

A software framework for analysing solid-state MAS NMR data

Solid-state magic-angle-spinning (MAS) NMR of proteins has undergone many rapid methodological developments in recent years, enabling detailed studies of protein structure, function and dynamics. Software development, however, has not kept pace with these advances and data analysis is mostly performed using tools developed for solution NMR which do not directly address solid-state specific issues. Here we present additions to the CcpNmr Analysis software package which enable easier identification of spinning side bands, straightforward analysis of double quantum spectra, automatic consideration of non-uniform labelling schemes, as well as extension of other existing features to the needs of solid-state MAS data. To underpin this, we have updated and extended the CCPN data model and experiment descriptions to include transfer types and nomenclature appropriate for solid-state NMR experiments, as well as a set of experiment prototypes covering the experiments commonly employed by solid-sate MAS protein NMR spectroscopists. This work not only improves solid-state MAS NMR data analysis but provides a platform for anyone who uses the CCPN data model for programming, data transfer, or data archival involving solid-state MAS NMR data

MESSAGEix-Materials v1.0.0: Representation of Material Flows and Stocks in an Integrated Assessment Model

Extracting and processing raw materials into products in industry is a substantial source of CO2 emissions, which currently lacks process detail in many integrated assessment models (IAMs). To broaden the space of climate change mitigation options and to include circular economy and material efficiency measures in IAM scenario analysis, we developed MESSAGEix-Materials module representing material flows and stocks within the MESSAGEix-GLOBIOM IAM framework. With the development of MESSAGEix-Materials, we provide a fully open-source model that can assess different industry decarbonization options under various climate targets for the most energy and emissions-intensive industries: Aluminium, iron and steel, cement, and petrochemicals. We illustrate the model’s operation with a baseline and mitigation (2 degrees) scenario setup and validate base year results for 2020 against historical datasets. We also discuss the industry decarbonization pathways and material stocks of the electricity generation technologies resulting from the new model features