optimade-maker 0.5.1

Tools for making OPTIMADE APIs from raw structural data.

optimade-maker

Tools for making OPTIMADE APIs from various formats of structural data (e.g. an archive of CIF files).

This repository contains the src/optimade-maker Python package and the corresponding CLI tool optimake that work towards this aim. Features include

• definition of a config file format (optimade.yaml) for annotating data archives to be used in the OPTIMADE ecosystem;
• conversion of the raw data into corresponding OPTIMADE types using pre-existing parsers (e.g., ASE for structures);
• conversion of the annotated data archive into an intermediate JSONLines file format that can be ingested into a database and used to serve a full OPTIMADE API.
• serving either an annotated data archive or a JSONLines file as an OPTIMADE API (using the optimade-python-tools reference server implementation).

Usage

See ./examples for a more complete set of supported formats and corresponding optimade.yaml config files.

Annotating with optimade.yaml

To annotate your structural data for optimade-maker, the data archive needs to be accompanied by an optimade.yaml config file. The following is a simple example for a zip archive (structures.zip) of cif files together with an optional property file (data.csv):

config_version: 0.1.0
database_description: Simple database

entries:
  - entry_type: structures
    entry_paths:
      - file: structures.zip
        matches:
          - cifs/*/*.cif
    # (optional) property file and definitions:
    property_paths:
      - file: data.csv
    property_definitions:
      - name: energy
        title: Total energy per atom
        description: The total energy per atom as computed by DFT
        unit: eV/atom
        type: float

Structure ids and property files

optimade-maker will assign an id for each structure based on its full path in the archive, following a simple deterministic rule: from the set of all archive paths, the maximum common path prefix and postfix (including file extensions) are removed. E.g.

structures.zip/cifs/set1/101.cif
structures.zip/cifs/set2/102.cif

produces ["set1/101", "set2/102"].

The property files need to either refer to these ids or the full path in the archive to be associated with a structure. E.g. a possible property csv file could be

id,energy
set1/101,2.5
structures.zip/cifs/set2/102.cif,3.2

Installing and running optimake

Install with

pip install optimade-maker

this will also make the optimake CLI utility available.

For a folder containing the data archive and the optimade.yaml file (such as in /examples), run

• optimake convert . to just convert the entry into the JSONL format (see below).
• optimake serve . to start the OPTIMADE API (this also first converts the entry, if needed);

For more detailed information see also optimake --help.

optimade-maker JSONLines Format

As described above, optimade-maker works via an intermediate JSONLines file representation of an OPTIMADE API (see also the corresponding issue in the specification). This file should provide enough metadata to spin up an OPTIMADE API with many different entry types. The format is as follows:

• First line must be a dictionary with the key x-optimade, containing a sub-dictionary of metadata (such as the OPTIMADE API version).
• Second line contains the info/structures endpoint.
• Third line contains the info/references endpoint, if present.
• Then each line contains an entry from the corresponding individual structure/reference endpoints.

{"x-optimade": {"meta": {"api_version": "1.1.0"}}}
{"type": "info", "id": "structures", "properties": {...}}
{"type": "info", "id": "references", "properties": {...}}
{"type": "structures", "id": "1234", "attributes": {...}}
{"type": "structures", "id": "1235", "attributes": {...}}
{"type": "references", "id": "sfdas", "attributes": {...}}

NOTE: the info/ endpoints in OPTIMADE v1.2.0 will include type and id as well.

Relevant links

• Roadmap and meeting notes
• OPTIMADE serialization format notes
• Flow diagram

Contributors

Initial prototype was created at the Paul Scherrer Institute, Switzerland in the week of 12th-16th June 2023.

Authors (alphabetical):

• Kristjan Eimre
• Matthew Evans
• Giovanni Pizzi
• Gian-Marco Rignanese
• Jusong Yu
• Xing Wang