AiiDA: Automated Interactive Infrastructure and Database for Computational Science

TL;DR

AiiDA is an open-source platform that automates, manages, and shares computational science workflows, ensuring reproducibility and provenance tracking through a graph-based data model, primarily for materials science.

Contribution

This paper introduces AiiDA, a novel platform that integrates automation, data management, environment control, and sharing to improve computational science practices.

Findings

AiiDA effectively tracks data provenance using directed acyclic graphs.

The platform manages remote computational resources transparently.

AiiDA facilitates reproducibility and sharing of scientific workflows.

Abstract

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.