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Releases: amkrajewski/mpdd-alignn

v0.1.6

25 Oct 14:14
@amkrajewski amkrajewski
v0.1.6
e9908b6
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v0.1.6 Latest
Latest

Minor Changes:

  • Pin torch==2.3.0 to navigate compatibility challenges caused by dgl and related libraries by @ricardonpa in #1

New Contributors

Full Changelog: v0.1.5...v0.1.6

Contributors

ricardonpa
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v0.1.5

11 Jul 20:12
@amkrajewski amkrajewski
v0.1.5
c988d4e
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v0.1.5

Minor Updates:

  • Updated dependency specification to resolve problems with alignn pinning older pydantic incompatible with dgl. pydantic was removed from top-level dependencies, too in favor of pydantic_settings actually used in testing.
  • Package metadata updates.

Full Changelog: v1.0.2...v1.0.5

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Initial Functional Release

19 Feb 02:28
@amkrajewski amkrajewski
v1.0.0
45b1c90
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Initial Functional Release

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.

  • 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.

  • 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]".

  • 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,
...

Full Changelog

v2024.1.14...v1.0.0

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