📍 ICIQ Library
⏰ 11.00 h
What AiiDA Can Do for You and How We Are Making it Even Better
Have you ever built a computational script for running calculations and lost track of the data you produced? Have you submitted your script to a high-performance cluster (HPC) and your job failed, so you had to restart the whole workflow? Have you spent hours fixing issues with running an ab initio code only to realize other people had already solved the exact same issues before? The “Automated Interactive Infrastructure and Database for Computational Science” (AiiDA) solves these problems and many more. AiiDA is an open-source Python code based on the four pillars of the ADES model – Automation, Data, Environment, and Sharing – with a strong focus on high-throughput performance, reproducibility, and provenance[1-4]. In this seminar, I will introduce AiiDA’s core concepts, including its provenance-based data model, workflow engine, and plugin system. The presentation will highlight how AiiDA facilitates reproducibility, data management, and collaboration in computational research. In addition, I will outline recent usability improvements, in particular new features that have been added in the recent v2.6 release[5]. Among others, these include a fully service-less installation, incremental profile backups, and the ability to mirror process data to disk. Lastly, I will provide a bird’s eye view of further developments in the AiiDA sphere, including the new workflow system, aiida-workgraph[6], as well as augmentative tools such as aiida-project[7], to make sure that you get the most out of your AiiDA journey.
[1] Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N. & Kozinsky, B., Comp. Mater. Sci.111, 218–230 (2016).
[2] Huber, S. P. et al. Sci. Data 7, 300 (2020).
[3] Uhrin, M., Huber, S. P., Yu, J., Marzari, N. & Pizzi, G., Comp. Mater. Sci. 187, 110086 (2021).
[4] https://aiida.readthedocs.io/projects/aiida-core/en/stable/intro/index.html#intro
[5] https://aiida.discourse.group/t/aiida-v2-6-released/423
[6] https://aiida-workgraph.readthedocs.io/en/stable/
[7] https://github.com/aiidateam/aiida-project
More information about the speaker here