/mpdd-alignn

MPDD Calculator for Atomistic Line Graph Neural Network Deployment

Primary LanguagePythonOtherNOASSERTION

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:

    [{
    'ALIGNN-JARVIS Bulk Modulus [GPa]': 98.06883239746094,
    'ALIGNN-JARVIS Exfoliation Energy [meV/atom]': 101.71208190917969,
    'ALIGNN-JARVIS Formation Energy [eV/atom]': -1.1146986484527588,
    'ALIGNN-JARVIS MBJ Bandgap [eV]': 0.5845542550086975,
    'ALIGNN-JARVIS Shear Modulus [GPa]': 39.18968963623047,
    'ALIGNN-MP Formation Energy [eV/atom]': -1.4002774953842163,
    'ALIGNN-MP PBE Bandgap [eV]': 1.074204921722412,
    'name': '9-Pb8O12.POSCAR'
},
{
    'ALIGNN-JARVIS Bulk Modulus [GPa]': 194.2947540283203,
    'ALIGNN-JARVIS Exfoliation Energy [meV/atom]': 362.1310729980469,
    'ALIGNN-JARVIS Formation Energy [eV/atom]': 0.010236039757728577,
    'ALIGNN-JARVIS MBJ Bandgap [eV]': 0.0064897798001766205,
    'ALIGNN-JARVIS Shear Modulus [GPa]': 85.74588775634766,
    'ALIGNN-MP Formation Energy [eV/atom]': -0.018119990825653076,
    'ALIGNN-MP PBE Bandgap [eV]': -0.00551827996969223,
    'name': '19-Fe4Ni26.POSCAR'
},
{
    'ALIGNN-JARVIS Bulk Modulus [GPa]': 185.35687255859375,
    'ALIGNN-JARVIS Exfoliation Energy [meV/atom]': 379.46417236328125,
    'ALIGNN-JARVIS Formation Energy [eV/atom]': 0.10529126971960068,
...

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 .

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.