MBI Material Database Concept

Due to the general demand for collecting, storing, and exchanging material parameters, we provide a text file based solution as well as several Python scripts and GUIs to read, change, and store material parameters.

Initially, we will only support YAML as data source, but the general design will allow also for other data sources, such as SQL databases or other file formats, e.g. XML or JSON.

YAML Format

An very simple example of a YAML file is given below:

meta:
  name: iron
  formula: Fe
  description: pure crystallien iron in beta phase
  comment: maybe there is something to say here
  last_updated: 2020-04-28
  references:
    @article{curtiss2013unicorn,
    author = "Curtiss, Michael and Becker, Iain and Bosman, Tudor and Doroshenko, Sergey and Grijincu, Lucian and Jackson, Tom and Kunnatur, Sandhya and Lassen, Soren and Pronin, Philip and Sankar, Sriram and others",
    title = "Unicorn: A system for searching the social graph",
    journal = "Proceedings of the VLDB Endowment",
    volume = "6",
    number = "11",
    pages = "1150--1161",
    year = "2013",
    publisher = "VLDB Endowment",
    doi = "10.14778/2536222.2536239"
    }
data:
  thermal_conductivity:
    curtiss2013unicorn:
      value: 80
      uncertainty: 1
      unit: J/m
      comment: some value comment here
  refractive_index:
    curtiss2013unicorn:
      value:
        wavelength:
          values: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...]
          arg_values: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...]
          arg_unit: nm
          comment: maybe one can write a fit of the data here
      unit: ''
      comment: n = n' + i k

First, there is meta section which holds meta-data of the material. Most importantly, the must be references defined, in order to link paramters to them. The references entrie must be a plain bibtex entry which can hold any type of reference and as many as necessary.

In the data section the actual physical parameters are declared. It is possible to have multiple values stored for a single parameter, by indexing each by a citation_key which must be defined above in the meta section. The actual parmaters, must have value and unit as well as optional uncertainty and comment.

In case of a functional dependence of a parameter, the name of the argument must be given as new index. The actual parameter values, the arg_values and arg_unit are mandatory. The comment is optional. With this format even multiple functional dependencies are possible from one and the same or multiple references.

Python Scripts

Although the YAML file format allows for human readability and does not require any programmatic access, we provide several Python modules in order to inteact with the material database.

We first stick to Python but other languages such as MATLAB can be implemented in the future as well.

We divide the modules into a Parser module which is dedicated for reading and writing data from and to e.g. a YAML file. The data must be provided as a dict in Python with a well defined structure.

The Material module implements a class for Material, Paramter, and References and should allow for easy interaction in a Python script with the material database.

The GUI module even builds a graphical interface to read, change, and create data files.

An example of programmatic access to the database could look like this:

import material_database as md

parser = md.PyYamlParser('/my/path/to/yaml/files')

iron = md.Material(parser.read_from_file(iron.yaml))

print(iron.thermal_conductivity)
.. 80