# AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

arXiv: https://arxiv.org/abs/2003.12476 · HTML: https://mlsanity.com/paper/2003.12476/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [El Agente: An Autonomous Agent for Quantum Chemistry](https://mlsanity.com/paper/2505.02484/) (2025)
- [ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback](https://mlsanity.com/paper/2402.10980/) (2024)
- [PET-MAD, a lightweight universal interatomic potential for advanced materials modeling](https://mlsanity.com/paper/2503.14118/) (2025)
- [Multitask methods for predicting molecular properties from heterogeneous data](https://mlsanity.com/paper/2401.17898/) (2024)
- [Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach](https://mlsanity.com/paper/2310.18702/) (2023)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [VASP Agent: An Agentic Framework for Autonomous First-principles Calculations](https://mlsanity.com/paper/2512.19458/) (2025)
- ["They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing](https://mlsanity.com/paper/2412.11483/) (2024)
- [Active Learning Enables Generation of Molecules that Advance the Known Pareto Front](https://mlsanity.com/paper/2501.02059/) (2025)
- [Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns](https://mlsanity.com/paper/2608.07637/) (2026)
- [Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy](https://mlsanity.com/paper/2109.11576/) (2021)
- [CateCom: a practical data-centric approach to categorization of computational models](https://mlsanity.com/paper/2109.13452/) (2021)
- [Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties](https://mlsanity.com/paper/2602.01358/) (2026)
- [From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics](https://mlsanity.com/paper/2603.13191/) (2026)
- [Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data](https://mlsanity.com/paper/2604.06230/) (2026)
- [Machine learning Hubbard parameters with equivariant neural networks](https://mlsanity.com/paper/2406.02457/) (2024)
- [Machine learning enhanced atom probe tomography analysis: a snapshot review](https://mlsanity.com/paper/2504.14378/) (2025)
- [ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows](https://mlsanity.com/paper/2509.15236/) (2025)
- [InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery](https://mlsanity.com/paper/2606.16133/) (2026)
- [ALKEMIE Agent: an autonomous platform for computational materials design](https://mlsanity.com/paper/2608.15776/) (2026)
- [Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay](https://mlsanity.com/paper/2610.16145/) (2026)
- [AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages](https://mlsanity.com/paper/2610.17110/) (2026)

## Abstract (arXiv)

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.