main

• 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.