AiiDAlab: on the route to accelerate science
Aliaksandr V.Yakutovich, Jusong Yu, Daniel Hollas, Edan Bainglass, Corsin Battaglia, Miki Bonacci, Lucas Fernandez Vilanova, Stephan Henne, Anders Kaestner, Michel Kenzelmann, Graham Kimbell, Jakob Lass, Fabio Lopes, Daniel G. Mazzone, Andres Ortega-Guerrero, Xing Wang

TL;DR
AiiDAlab is a web-based platform that simplifies complex computational workflows, enabling researchers across disciplines to accelerate scientific discovery while ensuring reproducibility and data management.
Contribution
The paper presents the evolution of AiiDAlab from a materials science tool to a versatile platform supporting multiple scientific disciplines and enhancing user accessibility and data management.
Findings
Adoption in quantum chemistry, atmospheric modeling, and battery research.
Enhanced user onboarding and resource access.
Integration with electronic lab notebooks for FAIR data principles.
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,…
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Taxonomy
TopicsMachine Learning in Materials Science · Scientific Computing and Data Management · Advanced Electron Microscopy Techniques and Applications
