AiiDA: automated interactive infrastructure and database for computational science

2015/04/30 by Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini +2 · 13 citations
Computer Science · Decision Sciences · Engineering · Materials Science · Physics and Astronomy · #Automation #Citizen science #Computational model #Computer science #Data science #Data sharing #Database #Dissemination #Distributed and Parallel Computing Systems #Engineering #Machine Learning in Materials Science #Scientific Computing and Data Management #Scientific instrument #Scope (computer science) #Simulation #Telecommunications #Workflow #World Wide Web #cond-mat.mtrl-sci #cs.SE #physics.comp-ph

paper · pdf · doi:10.1016/j.commatsci.2015.09.013

published as G. Pizzi, A. Cepellotti, R. Sabatini, N. Marzari, and B. Kozinsky, Comp. Mat. Sci 111, 218-230 (2016) · 30 pages, 7 figures

arxiv created 2015/09/08 · openalex publication_date 2015/10/02 · arxiv updated 2015/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

Citations

• The inverse band-structure problem of finding an atomic configuration with given electronic properties
• Predicting Crystal Structures with Data Mining of Quantum Calculations
• QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
• The NumPy array: a structure for efficient numerical computation

Cited by

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• M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
• Mobility of two-dimensional materials from first principles in an accurate and automated framework
• The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study
• Many-body perturbation theory calculations using the yambo code
• CateCom: a practical data-centric approach to categorization of computational models
• Boltzmann Transport in Nanostructures as a Friction Effect
• Photorealistic modelling of metals from first principles
• Precision and efficiency in solid-state pseudopotential calculations
• Automated high-throughput Wannierisation
• Benchmark and application of unsupervised classification approaches for univariate data

Related

• Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab
• An open framework for archival, reproducible, and transparent science
• Reproducible container solutions for codes and workflows in materials science
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• "Share and Enjoy": Publishing Useful and Usable Scientific Models