Metis scientific backend

Metis is an open scientific framework, materials data organizer, and collaborative online platform for the nanotechnology research. It was designed for the offline physical and online virtual autonomous laboratories dealing with the materials science. Metis is an AI-ready solution, aiming to bring the recent advances of computer science into a rather conservative area of new materials development and quality control. Metis currently focuses on the X-ray powder diffraction and atomistic simulations. Its development was started in 2021 in BASF (Ludwigshafen am Rhein, Germany) by Bernd Hinrichsen and Evgeny Blokhin.

This is the third part of the whole Metis infra: GUI ⇄ BFF ⇄ backend.

Metis backend presents minimalistic utility toolset in materials informatics and Flask-based CRUD-server for the nano-scale crystalline data, XRPD measurements, and cloud simulations.

Requirements

The basic requirements are Python, Numpy, and PostgreSQL. Note that the Numpy depends on the low-level Fortran numeric system libraries, which might be absent in your system. The Python dependency xylib-py requires C++ Boost stack to compile, install it on Debian with apt-get install libboost-all-dev.

A scientific cloud scheduler yascheduler is required for a separate cloud orchestration. An optional requirement is AiiDA, which can be linked to the cloud scheduler. The AiiDA is a Python framework for the complex scientific workflows, requiring PostgreSQL database and RabbitMQ message broker.

Thus, metis-backend consists of the 3 independent parts, each using PostgreSQL:

• metis-backend Python server per se
• aiida_core workflow engine
• yascheduler cloud scheduler

Optionally, a frontend server is Nginx (conf/nginx.conf goes to /etc/nginx), and all these guys are controlled by the Supervisor daemon (conf/supervisord.conf goes to /etc/supervisor).

Installation

Refer to conf/install.sh for installation of Nginx, PostgreSQL, RabbitMQ, and Supervisor, as well as configuring them. Run conf/install.sh and then modify global options conf/env.ini. Alternatively, feel free to install each component on your own (or all together in a container, see below).

The AiiDA can be installed and configured separately. First, a possibility for ssh-ing into a localhost should be ensured:

ssh-keygen -t rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
ssh $USER@localhost

Then a yascheduler plugin is configured (conf/yascheduler.conf goes to /etc/yascheduler). After that run yainit to start a yascheduler service. Then, if you have already run conf/install.sh script, it should be enought to do:

pip install aiida_core
bash conf/aiida_setup.sh

Then setup your virtual env, if needed, and install the Python requirements as simply aspip install -r requirements.txt.

Finally, apply the database schema: /data/pg/bin/psql -U postgres -d metis -f schema/schema.sql.

Running

Run supervisorctl status to see all the services in Supervisor.

One by one, all the parts are managed as follows:

• metis-backend server is started with index.py
• yascheduler is started simply with yascheduler command
• AiiDA is managed with verdi, e.g. verdi process list or verdi node show
• AiiDA daemon is started separately with verdi daemon start
• PostgreSQL database(s) can be seen with /data/pg/bin/psql -U postgres -l
• RabbitMQ is controlled with rabbitmqctl status
• (Nginx can be added to Supervisor as well depending on your taste)

Regular tasks

A script scripts/assign_phases.py should be run regularly to organize users' uploaded data, e.g. in cron job scheduler

*/5 * * * * /path/to/metis-backend/scripts/assign_phases.py

Run in containers

This is an experimental feature intended primarily for the development and testing.

It is assumed that you have the metis-backend, metis-bff, and metis-gui repositories cloned on the same level. Also, you need docker (or podman and podman-compose) installed.

Now, you can run docker compose up (podman-compose up) in metis-backend directory. This should start all dependencies and services.

metis-gui should be available at http://localhost:10000/

metis-bff should be available at http://localhost:3000/

Recognized data formats

Crystalline structures

• CIF
• POSCAR
• Optimade JSON

XRPD data

• XY and XYE patterns (TSV-alike)
• Bruker's RAW (binary)
• Bruker's Topas CLI modeling
• Synchrotron HDF5 NeXus (binary)

License

Copyright 2021-2023 BASF SE

BSD 3-Clause