# AiiDA: Automated Interactive Infrastructure and Database for Computational Science

arXiv: https://arxiv.org/abs/1504.01163 · HTML: https://mlsanity.com/paper/1504.01163/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2015.

## Built on by (most cited first, 20 of 20)

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- [Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores](https://mlsanity.com/paper/2208.05444/) (2022)
- [Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design](https://mlsanity.com/paper/2505.08159/) (2025)
- [Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models](https://mlsanity.com/paper/2108.13171/) (2021)
- [A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches](https://mlsanity.com/paper/2603.10992/) (2026)
- [AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D](https://mlsanity.com/paper/2603.27886/) (2026)
- [PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability](https://mlsanity.com/paper/2607.22573/) (2026)
- [AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software](https://mlsanity.com/paper/2608.20571/) (2026)
- [Agentic programs: an emerging form of scientific software in computational materials science](https://mlsanity.com/paper/2609.00795/) (2026)

## Abstract (arXiv)

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.