McStas and Mantid integration

McStas and Mantid are two well established software frameworks within the neutron scattering community. McStas has been primarily used for simulating the neutron transport of instruments, while Mantid has been primarily used for data reduction. We report here the status of our work done on the interoperability between the instrument simulation software McStas and the data reduction software Mantid. This provides a demonstration of how to successfully link together two software that otherwise have been developed independently, and in particular here show how this has been achieved for an instrument simulation software and a data reduction software. This paper will also provide examples of some of the expected future enhanced analysis that can be achieved from combining accurate instrument and sample simulations with software for correcting raw data. In the case of this work for raw data collected at large scale neutron facilities.Comment: 17 pages, 12 figures, POSTPRINT with proofs of article submitted to Journal of Neutron Researc

MDMC v0.1: Molecular Dynamics Monte Carlo

Software for producing dynamical models of materials at the atomic scale using classical molecular dynamics simulation, but simultaneously refining the potential energy parameters based on experimentally determined dynamical and structural information such as the dynamical structure factor. This version of the software has only been tested with argon data, and this first version is very much a pre-release version. The source code is available for download below. Also, a zip file, named mdmc_release_v0.1.zip, containing a Windows executable, user manual, scripts for plotting, example MDMC job files and argon test data is available for download https://github.com/MDMCproject/MDMC/releases

Polarised neutron diffraction measurements of PrBa2Cu3O6+x and the Bayesian statistical analysis of such data

The physics of the series PryY1-yBa2Cu3O6&plus;x, and ability of Pr to suppress superconductivity, has been a subject of frequent discussions in the literature for more than a decade. This thesis describes a polarised neutron diffraction (PND) experiment performed on PrBa2Cu3O6.24 designed to find out something about the electron structure. This experiment pushed the limits of what can be done using the PND technique. The problem is one of a limited number of measured Fourier components that need to be inverted to form a real space image. To accomplish this inversion the maximum entropy technique has been employed. In some cases, the maximum entropy technique has the ability to increase the resolution of ‘inverted’ data immensely, but this ability is found to depend critically on the choice of constants used in the method. To investigate this a Bayesian robustness analysis of the maximum entropy method is carried out, resulting in an improvement of the maximum entropy technique for analysing PND data. Some results for nickel in the literature have been re-analysed and a comparison is made with different maximum entropy algorithms. Equipped with an improved data analysis technique and carefully measured PND data for PrBa2Cu3O6.24 a number of new interesting features are observed, putting constraints on existing theoretical models of PryY1-yBa2Cu3O6&plus;x and leaving room for more questions to be answered.</p

Guidelines for collaborative development of sustainable data treatment software

Software development for data reduction and analysis at large research facilities is increasingly professionalized, and internationallycoordinated. To foster software quality and sustainability, and to facilitate collaboration, representatives from software groups of Europeanneutron and muon facilities have agreed on a set of guidelines for development practices, infrastructure, and functional and non-functionalproduct properties. These guidelines have been derived from actual practices in software projects from the EU funded consortium ‘Scienceand Innovation with Neutrons in Europe in 2020’ (SINE2020), and have been enriched through extensive literature review. Besides guiding thework of the professional software engineers in our computing groups, we hope to influence scientists who are willing to contribute their owndata treatment software to our community. Moreover, this work may also provide inspiration to scientific software development beyond theneutron and muon field

Dynamical Accuracy of Water Models on Supercooling

Molecular dynamics (MD) simulations are commonly used to explore the structural and dynamical properties of supercooled bulk water in the so-called "no man\u27s land" (NML) (150-227 K), where crystallization occurs almost instantaneously. This approach has provided significant insight into experimentally inaccessible phenomena. In this paper, we compare the dynamics of simulations using one-, three-, and four-body water models to experimentally measured quasielastic neutron scattering spectra. We show that the agreement between simulated and experimental data becomes substantially worse with a decrease in temperature toward the deeply supercooled regime. It was found that it is mainly the nature of the local dynamics that is poorly reproduced, as opposed to the macroscopic properties such as the diffusion coefficient. This strongly implies that the molecular mechanism describing the water dynamics is poorly captured in the MD models, and simulated structural and dynamical properties of supercooled water in NML must be interpreted with care

Quasi Elastic Neutron Scattering model library

This paper reports on the development of a collection of dynamical models of one-dimensional peak profile functions used to fit dynamic structure factors S (Q, ħω) of Quasi Elastic Neutron Scattering (QENS) data. The objective of this development is to create a maintainable and interoperable Python library with models reusable in other projects related to the analysis of data from Quasi Elastic Neutron Scattering experiments. The ambition is that the library also will serve as a platform where scientists can make their models available for others. We illustrate how the library can be used by newcomers to the field as well as by experts via different examples. These examples, provided as Jupyter notebooks, show how the QENS models can be integrated in the whole QENS data processing pipeline