AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
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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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- 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
- 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 32
- 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modelingcited by 23
- 2024Multitask methods for predicting molecular properties from heterogeneous datacited by 8
- 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approachcited by 5
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
- 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculationscited by 4
- 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensingcited by 3
- 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Frontcited by 2
- 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaignscited by 2
- 2021Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopycited by 1
- 2021CateCom: a practical data-centric approach to categorization of computational modelscited by 1
8 more
- 2026From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physicscited by 1
- 2024Machine learning Hubbard parameters with equivariant neural networkscited by 0
- 2025Machine learning enhanced atom probe tomography analysis: a snapshot reviewcited by 0
- 2025ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flowscited by 0
- 2026Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic propertiescited by 0
- 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation datacited by 0
- 2026InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discoverycited by 0
- 2026ALKEMIE Agent: an autonomous platform for computational materials designcited by 0
- 2026ALKEMIE Agent: an autonomous platform for computational materials design16 Aug 2026
- 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns7 Aug 2026
- 2026InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery15 Jun 2026
- 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data31 Mar 2026
- 2026From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics13 Mar 2026
- 2026Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties1 Feb 2026
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie18 Jan 2026
- 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculations22 Dec 2025
- 2025ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows17 Sep 2025
- 2025El Agente: An Autonomous Agent for Quantum Chemistry5 May 2025
- 2025Machine learning enhanced atom probe tomography analysis: a snapshot review19 Apr 2025
- 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modeling18 Mar 2025
8 more
- 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Front3 Jan 2025
- 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing16 Dec 2024
- 2024Machine learning Hubbard parameters with equivariant neural networks4 Jun 2024
- 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback15 Feb 2024
- 2024Multitask methods for predicting molecular properties from heterogeneous data31 Jan 2024
- 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach28 Oct 2023
- 2021CateCom: a practical data-centric approach to categorization of computational models28 Sep 2021
- 2021Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy23 Sep 2021
- 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 43
- 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculationscited by 4
- 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaignscited by 2
- 2026ALKEMIE Agent: an autonomous platform for computational materials designcited by 0
- 2026ALKEMIE Agent: an autonomous platform for computational materials design16 Aug 2026
- 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns7 Aug 2026
- 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculations22 Dec 2025
- 2025El Agente: An Autonomous Agent for Quantum Chemistry5 May 2025
- 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modelingcited by 23
- 2024Multitask methods for predicting molecular properties from heterogeneous datacited by 8
- 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approachcited by 5
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
- 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensingcited by 3
- 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Frontcited by 2
- 2021Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopycited by 1
- 2021CateCom: a practical data-centric approach to categorization of computational modelscited by 1
- 2026From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physicscited by 1
- 2024Machine learning Hubbard parameters with equivariant neural networkscited by 0
- 2025Machine learning enhanced atom probe tomography analysis: a snapshot reviewcited by 0
- 2025ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flowscited by 0
12 most cited of 15 All 15 (JSON)
- 2026InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery15 Jun 2026
- 2026Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data31 Mar 2026
- 2026From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics13 Mar 2026
- 2026Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties1 Feb 2026
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie18 Jan 2026
- 2025ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows17 Sep 2025
- 2025Machine learning enhanced atom probe tomography analysis: a snapshot review19 Apr 2025
- 2025PET-MAD, a lightweight universal interatomic potential for advanced materials modeling18 Mar 2025
- 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Front3 Jan 2025
- 2024"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing16 Dec 2024
- 2024Machine learning Hubbard parameters with equivariant neural networks4 Jun 2024
- 2024Multitask methods for predicting molecular properties from heterogeneous data31 Jan 2024
12 newest of 15 All 15 (JSON)