AiiDAlab - an ecosystem for developing, executing, and sharing scientific workflows

Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research software is often developed by scientists with limited experience in (and time for) user interface design, which can make research software difficult to install and use for novices. When combined with the increasing complexity of scientific workflows (involving many steps and software packages), setting up a computational research environment becomes a major entry barrier. AiiDAlab is a web platform that enables computational scientists to package scientific workflows and computational environments and share them with their collaborators and peers. By leveraging the AiiDA workflow manager and its plugin ecosystem, developers get access to a growing range of simulation codes through a python API, coupled with automatic provenance tracking of simulations for full reproducibility. Computational workflows can be bundled together with user-friendly graphical interfaces and made available through the AiiDAlab app store. Being fully compatible with open-science principles, AiiDAlab provides a complete infrastructure for automated workflows and provenance tracking, where incorporating new capabilities becomes intuitive, requiring only Python knowledge

Virtual computational chemistry teaching laboratories – hands-on at a distance

The COVID-19 pandemic disrupted chemistry teaching practices globally as many courses were forced online necessitating adaptation to the digital platform. The biggest impact was to the practical component of the chemistry curriculum – the so-called wet lab. Naively, it would be thought that computer-based teaching labs would have little problem in making the move. However, this is not the case as there are many unrecognised differences between delivering computer-based teaching in-person and virtually: software issues, technology and classroom management. Consequently, relatively few “hands-on” computational chemistry teaching laboratories are delivered online. In this paper we describe these issues in more detail and how they can be addressed, drawing on our experience in delivering a third-year computational chemistry course as well as remote hands-on workshops for the Virtual Winter School on Computational Chemistry and the European BIG-MAP project

Virtual Computational Chemistry Teaching Laboratories—Hands-On at a Distance

The COVID-19 pandemic disrupted chemistry teaching practices globally as many courses were forced online, necessitating adaptation to the digital platform. The biggest impact was to the practical component of the chemistry curriculum-the so-called wet lab. Naively, it would be thought that computer-based teaching laboratories would have little problem in making the move. However, this is not the case as there are many unrecognized differences between delivering computer-based teaching in-person and virtually: software issues, technology, and classroom management. Consequently, relatively few “hands-on” computational chemistry teaching laboratories are delivered online. In this paper, we describe these issues in more detail and how they can be addressed, drawing on our experience in delivering a thirdyear computational chemistry course as well as remote hands-on workshops for the Virtual Winter School on Computational Chemistry and the European BIG-MAP project

Materials Cloud, a platform for open computational science.

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible