mlsanityData to 7 Oct 2026

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

arXiv 2003.12476 ↗First posted 24 Mar 2020cs.DC cond-mat.mtrl-scicited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2020.

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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 4; 2025: 6; 2026: 7.

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  1. 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
  2. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 32
  3. 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modelingcited by 23
  4. 2024Multitask methods for predicting molecular properties from heterogeneous datacited by 8
  5. 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approachcited by 5
  6. 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
  7. 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculationscited by 4
  8. 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensingcited by 3
  9. 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Frontcited by 2
  10. 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaignscited by 2
  11. 2021Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopycited by 1
  12. 2021CateCom: a practical data-centric approach to categorization of computational modelscited by 1
8 more
  1. 2026From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physicscited by 1
  2. 2024Machine learning Hubbard parameters with equivariant neural networkscited by 0
  3. 2025Machine learning enhanced atom probe tomography analysis: a snapshot reviewcited by 0
  4. 2025ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flowscited by 0
  5. 2026Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic propertiescited by 0
  6. 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation datacited by 0
  7. 2026InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discoverycited by 0
  8. 2026ALKEMIE Agent: an autonomous platform for computational materials designcited by 0