Discover CURATED-COFs

App showcasing screening of the CURATED-COFs, live at https://www.materialscloud.org/discover/curated-cofs. Based on the [Structure-Property-Visualizer]https://github.com/materialscloud-org/structure-property-visualizer).

Inner working

For each COF we create a group, e.g., discover_curated_cofs/05001N2 that contains all the nodes that are relevant for that structure. These nodes have the extra TAG_KEY, which indicates the content of the node:

'orig_cif', 'orig_zeopp', 'dftopt', 'opt_cif_ddec', 'opt_zeopp', # CIF structures, DFT optimization, and pore analysis
'isot_co2', 'isot_n2', 'isotmt_h2', 'isot_ch4','isot_o2', 'kh_xe', 'kh_kr', 'kh_h2s', 'kh_h2o', # results of Isotherm work chain
'appl_pecoal', 'appl_peng', 'appl_h2storage', 'appl_ch4storage', 'appl_o2storage', 'appl_xekrsel', 'appl_h2sh2osel' # post-processing applications

Currently GROUP_DIR = "discover_curated_cofs/" and TAG_KEY = "tag4", but they may vary in the future. These groups are generated using the utility make_export/create_groups_export.py or they can be imported from the latest databases stored on Materials Cloud.

Installation and run

After activating your AiiDA environment:

git clone https://github.com/lsmo-epfl/discover-curated-cofs.git
cd discover-curated-cofs
pip install -e .     # install python dependencies
./prepare.sh         # download test data (run only once)

Download the latest database from Materials Cloud and import it in AiiDA:

verdi import export_discovery_cof_xxx.aiida

Finally, visualize the app:

bokeh serve --show detail details figure results select-figure

Docker deployment

pip install -e .
./prepare.sh
docker-compose build
docker-compose up
# open http://localhost:3245/cofs/select-figure