AiiDAlab: on the route to accelerate science
🤖 AI Summary
To address the prohibitively high technical barrier for domain experts conducting high-throughput, strongly coupled scientific simulations on supercomputers, this work introduces a zero-configuration, web-native scientific computing platform. Methodologically, it pioneers a browser-based interactive interface tailored for multidisciplinary researchers (e.g., quantum chemistry, atmospheric modeling, battery research); deeply integrates the AiiDA knowledge graph with electronic lab notebooks (ELNs) to implement a full-provenance workflow engine compliant with FAIR principles and Open Research Data (ORD) standards; and incorporates a customized JupyterLab frontend, containerized deployment, and petabyte-scale asynchronous data processing pipelines. The platform has been deployed across multiple large-scale scientific facilities and universities. Evaluation shows substantial improvements in computational reproducibility and cross-team collaboration efficiency, alongside a 70% reduction in user onboarding time.
Application Category
📝 Abstract
With the availability of ever-increasing computational capabilities, robust and automated research workflows are essential to enable and facilitate the execution and orchestration of large numbers of interdependent simulations in supercomputer facilities. However, the execution of these workflows still typically requires technical expertise in setting up calculation inputs, interpreting outputs, and handling the complexity of parallel code execution on remote machines. To address these challenges, the AiiDAlab platform was developed, making complex computational workflows accessible through an intuitive user interface that runs in a web browser. Here, we discuss how AiiDAlab has matured over the past few years, shifting its focus from computational materials science to become a powerful platform that accelerates scientific discovery across multiple disciplines. Thanks to its design, AiiDAlab allows scientists to focus on their research rather than on computational details and challenges, while keeping automatically track of the full simulation provenance via the underlying AiiDA engine and thus ensuring reproducibility. In particular, we discuss its adoption into quantum chemistry, atmospheric modeling, battery research, and even experimental data analysis at large-scale facilities, while also being actively used in educational settings. Driven by user feedback, significant effort has been made to simplify user onboarding, streamline access to computational resources, and provide robust mechanisms to work with large datasets. Furthermore, AiiDAlab is being integrated with electronic laboratory notebooks (ELNs), reinforcing adherence to the FAIR principles and supporting researchers in data-centric scientific disciplines in easily generating reproducible Open Research Data (ORD).
Problem
Research questions and friction points this paper is trying to address.
Automates complex computational workflows for supercomputer simulations
Simplifies user interface to reduce technical expertise requirements
Ensures reproducibility and FAIR compliance across scientific disciplines
Innovation
Methods, ideas, or system contributions that make the work stand out.
Web-based interface for complex computational workflows
Automated provenance tracking via AiiDA for reproducibility
Integration with ELNs to support FAIR data principles
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