Change_log.md

main

v9.0.0

Note: AGAT after this version (included) cannot load the well-trained model before. If you need to do so, please use v8.0.5: https://pypi.org/project/agat/8.0.5/

• Fix bugs when traing model with voigt stress tensor.
  • Add a node to edge layer: agat/model/model.py.
  • Message passing: agat/model/model.py.
• Fix a bug when saving model agat\lib\model_lib.py.
• Fix a bug when training model agat\model\fit.py.

v8.0.3

• Modify agat/lib/model_lib.py#L40-L45
• Modify agat/app/cata/high_throughput_predict.py#L208

v8.0.3

• Modify agat/lib/model_lib.py#L178-L193
• Add default parameter: vasp_bash_pathhigh_throughput_dft_calculation.py#L71; default_parameters.py#L242.
• Modify run_vasp() function: high_throughput_lib.py#L124-L149.
• Add transfer learning: default_parameters.py#L97. agat/model/fit.py#L169-L174
• Add split graphs: agat/data/build_dataset.py#L795-L824

v8.0.0

• Convert TensorFlow to PyTorch backend.
• Updata docs.

v7.14.0

• Add API for controling HP DFT calculation. agat/default_parameters.py
• Add mask_reversed_magnetic_moments in agat/default_parameters.py and agat/data/data.py
• Modify agat/data/data.py:
  • Include stress in the graph: agat/data/data.py#L273-L275, agat/data/data.py#L350-L352.
  • Update method of parsing the vasp data: agat/data/data.py#L610, agat/data/data.py#L625-L656, agat/data/data.py#L661-L675.
• Update docs.

v7.13.4

• Add agat/app/cata/high_throughput_dft_calculation.py.
• Shift atomic positions before fix bottom atoms: agat/app/cata/high_throughput_predict.py#L225-L227
• Add default_hp_dft_config to agat/default_parameters.py#L139-L246.
• Add agat/lib/HighThroughputLib.py.
• Add agat/lib/ModifyINCAR.py.
• Upgrade docs.

v7.13.3

• Using self-defined tf-based functions to calculate Pearson r: agat/lib/GatLib.py#L248-L259

  This self-defined function can handle ValueError: array must not contain infs or NaNs.

• Fix a bug: bug

• Clip optimizer grads: clipnorm=1.0

v7.13.2

• Fix bugs in high-throughput predict:

  • agat/app/cata/generate_adsorption_sites.py
  • agat/app/cata/high_throughput_predict.py
  • agat/app/cata/high_throughput_predict.py
  • agat/app/cata/high_throughput_predict.py
  • agat/app/GatApp.py
  • agat/default_parameters.py

• Deprecate redundant training configurations:

  • train_energy_model: agat/model/ModelFit.py and agat/model/ModelFit.py
  • train_force_model: agat/model/ModelFit.py and agat/model/ModelFit.py
  • new_energy_train
  • new_force_train
  • load_graphs_on_gpu

v7.13.1

• Fix a bug here: agat/model/ModelFit.py
• Load well-trained models: agat/model/GatEnergyModel.py and agat/model/GatForceModel.py
• Test with best model after training. agat/model/ModelFit.py and agat/model/ModelFit.py.

v7.13

• Raise exception if error occurs when parsing OUTCAR file. agat/data/data.py
• Remove os from the root name space. agat/init.py
• Fix a bug when build graphs. See agat/data/data.py and agat/data/data.py. Specifically, one needs to cast tf.tensor as np.array before building graph properties with a very large tensor. agat/data/data.py.
• Debug at these lines of agat/data/data.py: L553 and L585-L588.

v7.12.2

• Using relative import. For example: agat/init.py
• Update documentations.
• Import useful objects only. For example: agat/app__init__.py
• Return test MAE after training. agat/model/ModelFit.py and agat/model/ModelFit.py

v7.12.1

• Import pymatgen module when necessary. See agat/data/AtomicFeatures.py. This feature was changed back.
• Specify device when building graphs. See agat/app/GatApp.py, agat/data/data.py
• Add default gpu specification when building database. agat/default_parameters.py
• Attache distributions at dist.

v7.12

• Release pip wheel.
• Simplify packages. See v1.0.0 for more details of the first release.

v1.0.0

First release to reproduce results and support conclusions of Design High-Entropy Electrocatalyst via Interpretable Deep Graph Attention Learning.