Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917537110818816 |
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| author | Yakutovich, Aliaksandr V. Hollas, Daniel Bainglass, Edan Yu, Jusong Battaglia, Corsin Bonacci, Miki Vilanova, Lucas Fernandez Henne, Stephan Kaestner, Anders Kenzelmann, Michel Kimbell, Graham Lass, Jakob Lopes, Fabio Mazzone, Daniel G. Ortega-Guerrero, Andres Wang, Xing Marzari, Nicola Pignedoli, Carlo A. Pizzi, Giovanni |
| author_facet | Yakutovich, Aliaksandr V. Hollas, Daniel Bainglass, Edan Yu, Jusong Battaglia, Corsin Bonacci, Miki Vilanova, Lucas Fernandez Henne, Stephan Kaestner, Anders Kenzelmann, Michel Kimbell, Graham Lass, Jakob Lopes, Fabio Mazzone, Daniel G. Ortega-Guerrero, Andres Wang, Xing Marzari, Nicola Pignedoli, Carlo A. Pizzi, Giovanni |
| contents | With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to configure execution environments, define calculation inputs, interpret outputs, and manage the complexity of parallel code execution on remote machines. To address these challenges, we developed AiiDAlab, a Jupyter-based web platform powered by the AiiDA computational infrastructure that provides a framework for managing and automating computational workflows while ensuring reproducibility through full provenance tracking. Through a collection of open-source user-friendly applications, AiiDAlab enables scientists to set up, execute, and analyze complex computational workflows without interacting directly with the underlying technical details, allowing them to focus on their research questions. In this paper, we discuss how AiiDAlab has matured over the past few years, expanding beyond computational materials science and its AiiDA origins. We present recent developments towards integrating with electronic laboratory notebooks (ELNs) for FAIR-compliant data management, adoption in large-scale facilities for secure access to experimental data and analytical tools, and applications in educational settings. Together with community-driven efforts to simplify onboarding, improve access to computational resources, and support large-scale data workflows, these advancements position AiiDAlab as a powerful platform for accelerating scientific discovery and fostering collaboration across disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab Yakutovich, Aliaksandr V. Hollas, Daniel Bainglass, Edan Yu, Jusong Battaglia, Corsin Bonacci, Miki Vilanova, Lucas Fernandez Henne, Stephan Kaestner, Anders Kenzelmann, Michel Kimbell, Graham Lass, Jakob Lopes, Fabio Mazzone, Daniel G. Ortega-Guerrero, Andres Wang, Xing Marzari, Nicola Pignedoli, Carlo A. Pizzi, Giovanni Distributed, Parallel, and Cluster Computing Materials Science With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to configure execution environments, define calculation inputs, interpret outputs, and manage the complexity of parallel code execution on remote machines. To address these challenges, we developed AiiDAlab, a Jupyter-based web platform powered by the AiiDA computational infrastructure that provides a framework for managing and automating computational workflows while ensuring reproducibility through full provenance tracking. Through a collection of open-source user-friendly applications, AiiDAlab enables scientists to set up, execute, and analyze complex computational workflows without interacting directly with the underlying technical details, allowing them to focus on their research questions. In this paper, we discuss how AiiDAlab has matured over the past few years, expanding beyond computational materials science and its AiiDA origins. We present recent developments towards integrating with electronic laboratory notebooks (ELNs) for FAIR-compliant data management, adoption in large-scale facilities for secure access to experimental data and analytical tools, and applications in educational settings. Together with community-driven efforts to simplify onboarding, improve access to computational resources, and support large-scale data workflows, these advancements position AiiDAlab as a powerful platform for accelerating scientific discovery and fostering collaboration across disciplines. |
| title | Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab |
| topic | Distributed, Parallel, and Cluster Computing Materials Science |
| url | https://arxiv.org/abs/2512.22173 |