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Workshops

Future Technologies in Automated Atomistic Simulations

June 8, 2015 to June 10, 2015
Location : CECAM-HQ-EPFL, Lausanne, Switzerland
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   Visa requirements

Organisers

  • Dane Morgan (University of Wisconsin - Madison, USA)
  • Nicola Marzari (Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland)
  • Claudia Draxl (Humboldt University Berlin, Germany)
  • Kristin Persson (Lawrence Berkeley National Laboratory, USA)

Supports

   CECAM

   Psi-k

   NSF SI2-SSI ACI-Award 1148011

   MARVEL

Description

The deadline for applications will be Thursday 30 April 2015 - participants will be informed soon afterwards of acceptance. Post-deadline applications will also be allowed.

 

The workshop will focus on the following key areas for developing the field of automated atomistic simulations (AAS)
1. Support best practices: Tools for AAS must choose computing languages (e.g., python vs. C++), databases (e.g., mongodb vs. SQL approaches), development and distribution channels (e.g., HubZero vs. pypi vs. github vs. web pages), interfaces (command line vs. GUI), and a many others. These choices are critical to the success of a project and often require knowledge from computer science and web development that is lacking in the materials and chemistry communities. There is therefore an enormous amount to be gained by sharing what works among those involved in this type of work.
2. Reduce redundancy: Many researchers are developing tools that perform the same function, from managing workflows to finding symmetry. The community will be far more efficient if it can be made aware what tools are available and how reuse can be done most effectively.
3. Develop standards: At present file formats, metadata and data sharing approaches, basic code objects, and many other choices are being made locally by each group. A discussion among the community stakeholders of where standardization is needed will greatly increase efficiency of the tools being developed.
4. Integrate databases: An enormous value can be realized if data in available in just one or a few databases, as this makes it easy to avoid redoing calculations, increases standardization and data compatibility, and greatly enhances opportunities for datamining. The AAS community would greatly benefit from assessing its database needs and forming integrated efforts.
5. Roadmap key needs: At present there is not community agreement about what tools are most critical to develop and how we can, as a group, develop them most efficiently. A roadmap of what should be automated (e.g., obtaining structural energies), where fundamental development is needed (e.g., in charged surfaces defects), and what types of cyberinfrastructure is needed (e.g., high-throughput computing clusters or databases) would be very useful to guide those entering the field and funding organizations. Many of these needs may require a cooperative effort of the community to be realized.
6. Brainstorm future opportunities: A blue sky discussion of what is possible with AAS and where it might go, from moving into mesoscale tools to citizen science opportunities to integration with multiscale frameworks, would be valuable for helping the community see beyond the immediate needs of the field. Such ideas are likely too far in the future to be part of a roadmap but can form the foundations of cutting edge work in this field going forward.

References

[1] S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, and G. Ceder, Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis, Computational Materials Science 68, p. 314-319 (2013).
[2] G. Ceder and K. Persson, The Materials Project: A Materials Genome Approach, http://www.materialsproject.org (2010).
[3] S. R. Bahn and K. W. Jacobsen, An object-oriented scripting interface to a legacy electronic structure code, Comput. Sci. Eng 4, p. 56-66 (2002).
[4] D. H. Landis, J.; Nestorov, S.; Greeley, J.; Dulak, M.; Bligaard, T.; Norskov, J.; Jacobsen, K., The Computational Materials Repository, Computing in Science & Engineering PP, p. 1 (2012).
[5] A. Van de Walle and G. Ceder, Automating First-Principles Phase Diagram Calculations, J. of Phase Equilibria 23, p. 348-359 (2002).
[6] A. van de Walle, M. Asta, and G. Ceder, The Alloy Theoretic Automated Toolkit: A user guide, Calphad-Computer Coupling of Phase Diagrams and Thermochemistry 26, p. 539-553 (2002).
[7] S. Curtarolo, W. Setyawan, G. L. W. Hart, M. Jahnatek, R. V. Chepulskii, R. H. Taylor, S. D. Wanga, J. K. Xue, K. S. Yang, O. Levy, M. J. Mehl, H. T. Stokes, D. O. Demchenko, and D. Morgan, AFLOW: An automatic framework for high-throughput materials discovery, Computational Materials Science 58, p. 218-226 (2012).
[8] T. Angsten, T. Mayeshiba, H. Wu, and D. Morgan, Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures, New Journal of Physics 16, p. 015018 (2014).
[9] N. Marzari, Automated Interactive Infrastructure and Database for Atomistic simulations (AiiDA), 2014, http://www.aiida.net/.