mlsanityData to 7 Oct 2026

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

arXiv 1504.01163 ↗First posted 5 Apr 2015physics.comp-ph cond-mat.mtrl-sci cs.SEcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2015.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 0; 2025: 5; 2026: 10.

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  1. 2022Data-driven discovery of novel 2D materials by deep generative modelscited by 18
  2. 2025MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modelingcited by 7
  3. 2025The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capturecited by 7
  4. 2026El Agente Quntur: A research collaborator agent for quantum chemistrycited by 7
  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. 2026TritonDFT: Automating DFT with a Multi-Agent Frameworkcited by 5
  8. 2025Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loopcited by 4
  9. 2025Active Learning Enables Generation of Molecules that Advance the Known Pareto Frontcited by 2
  10. 2026AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculationscited by 2
  11. 2026Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaignscited by 2
  12. 2021CateCom: a practical data-centric approach to categorization of computational modelscited by 1
8 more
  1. 2022Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophorescited by 1
  2. 2025Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Designcited by 1
  3. 2021Functional Nanomaterials Design in the Workflow of Building Machine-Learning Modelscited by 0
  4. 2026A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searchescited by 0
  5. 2026AI-ready design of realistic 2D materials and interfaces with Mat3ra-2Dcited by 0
  6. 2026PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stabilitycited by 0
  7. 2026AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Softwarecited by 0
  8. 2026Agentic programs: an emerging form of scientific software in computational materials sciencecited by 0