AiiDA: automated interactive infrastructure and database for computational science

Giovanni Pizzi; Andrea Cepellotti; Riccardo Sabatini; Nicola Marzari; Boris Kozinsky

doi:10.1016/j.commatsci.2015.09.013

journal article Open Access Jan 01, 2016

AiiDA: automated interactive infrastructure and database for computational science

Giovanni Pizzi

Andrea Cepellotti Riccardo Sabatini Nicola Marzari

Boris Kozinsky

Computational Materials Science Vol. 111 pp. 218-230 · Elsevier BV

View at Publisher Save 10.1016/j.commatsci.2015.09.013

Topics

No keywords indexed for this article. Browse by subject →

References

69

[1]

The top 100 papers

Richard Van Noorden, Brendan Maher, Regina Nuzzo

Nature 2014 10.1038/514550a

[2]

Franceschetti Nature (1999) 10.1038/46995

[3]

Jóhannesson Phys. Rev. Lett. (2002) 10.1103/physrevlett.88.255506

[4]

Curtarolo Phys. Rev. Lett. (2003) 10.1103/physrevlett.91.135503

[5]

Curtarolo Nat. Mater. (2013) 10.1038/nmat3568

[6]

Villars J. Alloys Compd. (2004) 10.1016/j.jallcom.2003.08.058

[7]

Zarkevich Complexity (2006) 10.1002/cplx.20117

[8]

da Silveira Comput. Phys. Commun. (2008) 10.1016/j.cpc.2007.09.001

[9]

Yuan Comput. Sci. Discov. (2010) 10.1088/1749-4699/3/1/015004

[10]

Jain Comput. Mater. Sci. (2011) 10.1016/j.commatsci.2011.02.023

[11]

Adams J. Cheminformatics (2011) 10.1186/1758-2946-3-38

[12]

Curtarolo Comput. Mater. Sci. (2012) 10.1016/j.commatsci.2012.02.002

[13]

Landis Comput. Sci. Eng. (2012) 10.1109/mcse.2012.16

[14]

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

James E. Saal, Scott Kirklin, Muratahan Aykol et al.

JOM 2013 10.1007/s11837-013-0755-4

[15]

http://www.eudat.eu/.

[16]

http://www.nomad-repository.eu/.

[17]

https://www.docker.com/.

[18]

http://www.djangoproject.com/.

[19]

http://www.sqlalchemy.org/.

[20]

http://rubyonrails.org/.

[21]

An object-oriented scripting interface to a legacy electronic structure code

S.R. Bahn, K.W. Jacobsen

Computing in Science & Engineering 2002 10.1109/5992.998641

[22]

Ong Comput. Mater. Sci. (2013) 10.1016/j.commatsci.2012.10.028

[23]

Murray-Rust J. Chem. Inf. Comput. Sci. (2003) 10.1021/ci0256541

[24]

http://www.crystallography.net/tcod/.

[25]

http://www.celeryproject.org/.

[26]

http://supervisord.org/.

[27]

http://www.ogf.org.

[28]

P. Tröger, H. Rajic, A. Haas,, P. Domagalski, in: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), Rio de Janeiro, Brazil, pp. 619–626. <http://www.drmaa.org/>.

[29]

http://www.unicore.eu/.

[30]

http://gc3pie.googlecode.com/.

[31]

http://www.adaptivecomputing.com/products/open-source/torque/.

[32]

http://www.pbsworks.com/Product.aspx?id=1.

[33]

https://computing.llnl.gov/linux/slurm/.

[34]

http://gridscheduler.sourceforge.net/.

[35]

https://github.com/paramiko/paramiko.

[36]

Moreau Future Gener. Comput. Syst. (2011) 10.1016/j.future.2010.07.005

[37]

http://www.mysql.com/.

[38]

http://www.postgresql.org/.

[39]

http://www.sqlite.org/.

[40]

http://www.neo4j.org/.

[41]

Dong Int. J. Inf. Technol. (1999)

[42]

http://spglib.sourceforge.net.

[43]

The NumPy Array: A Structure for Efficient Numerical Computation

Stéfan van der Walt, S. Chris Colbert, Gael Varoquaux

Computing in Science & Engineering 2011 10.1109/mcse.2011.37

[44]

Setyawan Comput. Mater. Sci. (2010) 10.1016/j.commatsci.2010.05.010

[45]

http://www.fiz-karlsruhe.com/icsd.html.

[46]

Gražulis Nucleic Acids Res. (2012) 10.1093/nar/gkr900

[47]

IPython: A System for Interactive Scientific Computing

Fernando Perez, Brian E. Granger

Computing in Science & Engineering 2007 10.1109/mcse.2007.53

[48]

http://galaxyproject.org.

[49]

https://kepler-project.org.

[50]

http://www.knime.org.

Showing 50 of 69 references

Cited By

506

Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design

Jiaxiang Li, Junwei Feng · 2026

npj Computational Materials

Starrydata: from published plots to shared materials data

Yukari Katsura, Masaya Kumagai · 2025

Science and Technology of Advanced...

Machine learning and data-driven methods in computational surface and interface science

Lukas Hörmann, Wojciech G. Stark · 2025

npj Computational Materials

Ab Initio Calculations of Longitudinal Electrical Conductivity Using a Wannier-based Coherent Potential Approximation

Shota Namerikawa, Takashi Koretsune · 2025

Journal of the Physical Society of...

Wannier-function software ecosystem for materials simulations

Antimo Marrazzo, Sophie Beck · 2024

Reviews of Modern Physics

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Matthew L. Evans, Johan Bergsma · 2024

Digital Discovery

First-principles Phonon Calculations with Phonopy and Phono3py

Atsushi Togo · 2023

Journal of the Physical Society of...

Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Jing Lin, Zhimeng Liu · 2023

Nano Today

Brokering between tenants for an international materials acceleration platform

Monika Vogler, Jonas Busk · 2023

Matter

Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations

D.E.P. Klenam, T.K. Asumadu · 2023

Results in Materials

A universal graph deep learning interatomic potential for the periodic table

Chi Chen, Shyue Ping Ong · 2022

Nature Computational Science

Toward autonomous materials research: Recent progress and future challenges

Joseph H. Montoya, Muratahan Aykol · 2022

Applied Physics Reviews

Numerical quality control for DFT-based materials databases

Christian Carbogno, Kristian S. Thygesen · 2022

npj Computational Materials

Accurate Electronic Properties and Intercalation Voltages of Olivine-Type Li-Ion Cathode Materials from Extended Hubbard Functionals

Iurii Timrov, Francesco Aquilante · 2022

PRX Energy

Effect of an axial ligand on the self-assembly of molecular platforms

Chao Li, Xiangzhi Meng · 2022

Physical Chemistry Chemical Physics

ALKEMIE: An intelligent computational platform for accelerating materials discovery and design

Guanjie Wang, Liyu Peng · 2021

Computational Materials Science

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Xin-Gang Zhao, Kun Zhou · 2021

Science Bulletin

Perovskite-inspired materials for photovoltaics and beyond—from design to devices

Yi-Teng Huang, Seán R Kavanagh · 2021

Nanotechnology

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Aditya Nandy, Chenru Duan · 2021

Chemical Reviews

MyQueue: Task and workflow scheduling system

Jens Mortensen, Morten Gjerding · 2020

The Journal of Open Source Software

Metrics

506

Citations

69

References

Details

Published: Jan 01, 2016
Vol/Issue: 111
Pages: 218-230
License: View

Authors

Funding

Office of Science of the U.S. Department of Energy Award: DE-AC05-00OR22725

Laboratory of Theory and Simulation of Materials (THEOS)

Swiss National Centre for Competence in Research on “Computational Design and Discovery of Novel Materials” (NCCR MARVEL)

Swiss National Supercomputing Centre (CSCS)

Cite This Article

Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini, et al. (2016). AiiDA: automated interactive infrastructure and database for computational science. Computational Materials Science, 111, 218-230. https://doi.org/10.1016/j.commatsci.2015.09.013

AiiDA: automated interactive infrastructure and database for computational science

You May Also Like