AiiDA is a flexible and scalable informatics’ infrastructure to manage, preserve, and disseminate the simulations, data, and workflows of modern-day computational science. Able to store the full provenance of each object, and based on a tailored database built for efficient data mining of heterogeneous results, AiiDA gives the user the ability to interact seamlessly with any number of remote HPC resources and codes, thanks to its flexible plugin interface and workflow engine for the automation of complex sequences of simulations.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Jack D. Sundberg, Siona S. Benjamin, Lauren M. McRae, Scott C. Warren: Simmate: a framework for materials science (2022) not zbMATH
- Katarina Brlec; Daniel W. Davies; David O. Scanlon: Surfaxe: Systematic surface calculations (2021) not zbMATH
- Morten Gjerding, Thorbjørn Skovhus, Asbjørn Rasmussen, Fabian Bertoldo, Ask Hjorth Larsen, Jens Jørgen Mortensen, Kristian Sommer Thygesen: Atomic Simulation Recipes - a Python framework and library for automated workflows (2021) arXiv
- Martin Uhrin; Sebastiaan P. Huber: kiwiPy: Robust, high-volume, messaging for big-data and computational science workflows (2020) not zbMATH
- Mortensen, J., Gjerding, M., Thygesen, K.: MyQueue: Task and work ow scheduling system (2020) not zbMATH
- Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E: DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models (2019) arXiv
- Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini, Nicola Marzari, Boris Kozinsky: AiiDA: Automated Interactive Infrastructure and Database for Computational Science (2015) arXiv