mpdd-alignn 1.3.0

Our version of the NIST-JARVIS ALIGNN optimized in terms of model performance and to some extent reliability, for large-scale deployments over the MPDD infrastructure by Phases Research Lab.

MPDD - ALIGNN Calculator

This tool is a modified version of the NIST-JARVIS ALIGNN optimized in terms of model performance and to some extent reliability, for large-scale deployments over the MPDD infrastructure by Phases Research Lab.

Critical Changes

Key modifications that were made here:

• A set of models of interest has been selected and defined in config.yaml for consistency, readability, and easy tracking. These are the models which will be populating MPDD.

• Dependency optimizations for running models, skipping by default installation of several packages needed only for training and auxiliary tasks. Full set can still be installed by pip install "mpdd-alignn[full]".

• The process of model fetching was far too slow using pretrained.get_figshare_model(); thus, we reimplemented it similar to pySIPFENN by multi-threading connection to Figshare via pysmartdl2 we maintain, and parallelize the process on per-model basis. Model download is now 7 times faster, fetching all 7 default models in 6.1 vs 41.4 seconds.

• Optimized what is included in the built package. Now, its package size is reduced 33.5 times, from 21.7MB to 0.65MB.

• Streamlined operation, where we can get results for a directory of POSCARS for all default models in just 3 quick lines

  from alignn import pretrained
  pretrained.download_default_models()
  result = pretrained.run_models_from_directory('example.SigmaPhase', mode='serial')
  

  Which give us neat:

  [
      {
          'name': '22-Fe10Ni20.POSCAR',
          'ALIGNN-Alexandria Bandgap [eV]': 0.001391,
          'ALIGNN-Alexandria Formation Energy [eV/atom]': 0.095294,
          'ALIGNN-Alexandria Volume Per Atom [A^3]': 11.140231,
          'ALIGNN-JARVIS Bulk Modulus [GPa]': 183.945847,
          'ALIGNN-JARVIS Exfoliation Energy [meV/atom]': 350.855591,
          'ALIGNN-JARVIS Formation Energy [eV/atom]': 0.027578,
          'ALIGNN-JARVIS MBJ Bandgap [eV]': 0.017667,
          'ALIGNN-JARVIS Shear Modulus [GPa]': 74.540077,
          'ALIGNN-MP Formation Energy [eV/atom]': -0.045874,
          'ALIGNN-MP PBE Bandgap [eV]': 0.01164,
      },
      {
          'name': '2-Fe8Ni22.POSCAR',
          'ALIGNN-Alexandria Bandgap [eV]': 0.001679,
          'ALIGNN-Alexandria Formation Energy [eV/atom]': 0.25086,
          'ALIGNN-Alexandria Volume Per Atom [A^3]': 10.656669,
          'ALIGNN-JARVIS Bulk Modulus [GPa]': 187.983017,
          'ALIGNN-JARVIS Exfoliation Energy [meV/atom]': 352.69455,
          'ALIGNN-JARVIS Formation Energy [eV/atom]': 0.025119,
          'ALIGNN-JARVIS MBJ Bandgap [eV]': 0.010531,
          'ALIGNN-JARVIS Shear Modulus [GPa]': 80.09848,
          'ALIGNN-MP Formation Energy [eV/atom]': -0.042081,
          'ALIGNN-MP PBE Bandgap [eV]': 0.019553,
      },
      {
          'name': '11-Fe10Ni20.POSCAR',
          'ALIGNN-Alexandria Bandgap [eV]': 0.001165,
          'ALIGNN-Alexandria Formation Energy [eV/atom]': 0.217117,
          'ALIGNN-Alexandria Volume Per Atom [A^3]': 10.583747,
      ...
  

ALIGNN Compatibility and Install

In general, we tried to retain full compatibility with the original ALIGNN, so this should be a drop-in replacement. You have to simply:

pip install mpdd-alignn

or (as recommended) clone this repository and

pip install -e .

DGL Issues

On some platforms, pip may struggle to get the dgl from PyPI that is compatible with the torch version you need for other dependencies and so on. In such case, we recommend to install lightweight (CPU) version of torch and matching dgl that should work well together by:

pip install torch==2.3.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

and

pip install  dgl -f https://data.dgl.ai/wheels/torch-2.3/repo.html

adjusting the torch version in both to your needs.

Contributions

Please direct all contributions to the ALIGNN repository. We will be synching our fork with them every once in a while and can do it quickly upon reasonable request.

The only contributions we will accept here are:

• Expanding the list of default models.
• Performance improvements to our section of the code.