Baler -- Machine Learning Based Compression of Scientific Data

Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle. We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data.Comment: 10 pages and 6 figures, excluding appendi

Automated Machine Learning Workflows for Fusion Power Plant Design

The need to meet increasing global energy demand and also address 2050 net zero targets is placing fusion energy in the spotlight. The authors are investigating the ad- vances in digital technology necessary to deliver a fusion power plant by 2040. We are currently evaluating the use of the Nvidia Omniverse platform for engineering, with the specific need to consider fusion power plants as a whole system. In a plant that uses a magnetically confined plasma, fusion generates neutrons which pass through the whole machine. The design process needs to consider how to protect some sys- tems from the neutrons, whilst in other parts of the machine, the neutrons can be used to generate new fuel. It is difficult to compartmentalise the design process for these two opposing requirements. Therefore a requirement for conceptual design is the ability to carry out fast physics-informed simulations for many coupled systems at the same time. This paper describes how the authors have integrated the Galaxy workflow engine with the Omniverse, automating the execution of a suite of containerised open source software applications that can be used for training surrogate models. Automa- tion is essential if AI and machine learning is to be leveraged in the design of complex engineering systems

Actionable workflows for fusion neutronics simulation.

Neutronics simulations are complex and typically require significant effort in setting up multiple pre- and post-processing steps, such as preparing data and geometry before the neutronics simulation can be carried out. From the end user point of view, many complex tools must be mastered in order to run the simulations, creating a barrier of entry to a field that needs to transition from science to engineering. Different versions of the tools used and customisation of processing steps can also lead to reproducibility issues, and the data produced could often be better managed.We propose setting up standard packages (i.e. OpenMC and Paramak) as interoperable tools that can be linked up to create an automated simulation process, to be executed using a workflow engine (i.e. Galaxy). The integrated tools can then be (re)configured into scalable, actionable workflows that are FAIR; findable, accessible, interoperable and reusable. The chosen workflow engine provides a simple and accessible interface with many added benefits, such as capturing metadata, documenting what simulation has been executed, when, by whom, how and why. The selected workflow engine also enables automatic scheduling on distributed and high performance computing systems.The presentation will use a spherical tokamak case study to show how this approach can be used to orchestrate neutronics simulations. The authors will first show how individual tools can be put together as automated workflows. By presenting the results of a neutronics simulation carried out in this way, the authors will then highlight the simplicity and added benefits of workflows.The work is aimed at the neutronics community but especially newcomers or those outside the community (such as SMEs or young researchers) who wish to run basic simulations but are unfamiliar with the tools used in the sector

The Specimen Data Refinery: Using a scientific workflow approach for information extraction

Over the past three years, we have been developing the Specimen Data Refinery (SDR) to automate the extraction of data from specimen images as part of the SYNTHESYS project (Walton et al. 2020). The SDR provides an easy to deploy, open source, web-based interface to multiple workflows that enable a user to create new or enhance existing natural history specimen records. The SDR uses the Galaxy workflow platform as the basis for managing data analysis, and where possible, using existing Galaxy community tools and approaches (Jalili et al. 2020, Hardisty et al. 2022). We have developed a library of domain-specific tools including semantic segmentation, optical character recognition, hand-written text recognition, barcode reading and natural language processing. These tools have been designed to work on standardised images of specimens, specifically herbarium sheets, pinned insects and microscope slides.In this presentation, we provide our technical approach in developing the SDR, including the Galaxy workflow platform, application deployment, and tool interoperability, using FAIR digital objects (e.g., RO-Crates and openDigital Specimen objects (Soiland-Reyes et al. 2022, Addink and Hardisty 2020)). We present an evaluation of the tools, including segmentation, text recognition, and others, and the new challenges in using the resulting data from both a technical and social perspective

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Specimen Data Refinery (SDR) is a developing platform for automating transcription of specimens from natural history collections (Hardisty et al. 2022). SDR is based on computational workflows and digital twins using FAIR Digital Objects. We show our recent experiences with building SDR using the Galaxy workflow system and combining two FDO methodologies with open digital specimens (openDS) and RO-Crate data packaging. We suggest FDO improvements for incremental building of digital objects in computational workflows

Exciton effects in perovskite nanocrystals

Nanocrystals (NCs) of perovskite materials have recently attracted great research interest because of their outstanding properties for optoelectronic applications, as evidenced by the increasing number of publications on laboratory scale devices. However, in order to achieve the commercial realisation of these devices, an in-depth understanding of the charge dynamics and photo-physics in these novel materials is required. These dynamics are affected by material composition but also by their size and morphology due to quantum confinement effects. Advances in synthesis methods have allowed nanostructures to be produced with enhanced confinement and structural stability, enhancing the efficiency of energy funnelling and radiative recombination and so resulting in more efficient light emitting devices. In addition, photovoltaics could greatly benefit from the exploitation of these materials not only through their deployment in tandem cell architectures but from the use of multiple exciton generation in these NCs. These systems also offer the opportunity to study quantum effects relating to interactions of excited states within and between NCs. Properties and behaviour that includes an enhanced Rashba effect, superfluorescence, polariton lasing, Rydberg exciton polariton condensates, and antibunched single photon emission have been observed in a single metal halide perovskite NC. The further study of these in NC systems will shed new light on the fundamental nature of their excited states, their control and exploitation. In this perspective, we give an overview of these effects and provide an outlook for the future of perovskite NCs and their devices.</p