v0.1.1

Fixed

• Bayesian optimization for sigma hyperparameter optimization changed how to specify gp_params.
• Pydantic had API changes that affected qcelemental, so we have to specify pydantic<2.0.0 for now.

v0.1.0

Added

• Iterative solver for GDML training on larger systems (changes up to v1.0.0).
• Additional documentation on obtaining many-body data.
• Some many-body expansion logging (for debugging).
• Logger documentation.
• Function to change mbGDML log levels.
• Ability of ASE calculator to update periodic cell on mbePredict object.
• Preliminary virial stress algorithms and stress module.
• Switching function module for alchemical scaling.
• Documentation example of an ASE optimization under periodic boundary conditions.
• Structure generation module using packmol.
• Provide explicit example of model comp ids in documentation.

Changed

• GDML solvers are put into their own modules.
• Updated prediction set documentation.
• Unified use of ray by always having use_ray option and defaulting n_workers to 1.

Fixed

• utils.center_structures sometimes repeated the array incorrectly.
• Explicitly use 3DMol v1.8.0 for documentation.

v0.0.4

Added

• mbgdml.descriptors.Criteria class for setting up structure descriptors and cutoffs for models.
• Model analysis with Matérn covariance function.
• mbe_contrib tests.
• Radial distribution function analysis
• Periodic many-body expansions with the minimum image convention.
• Many-body alchemical parameter.

Changed

• Unified use of Z, z, R, and r.
• dataSet to DataSet
• Switch to qcelemental for atom properties.
• Do not restrict sigma_bounds for Bayesian optimization after initial grid search.
• mbgdml.criteria is now mbgdml.descriptors with a modular handling of descriptors and cutoffs.
• Provide changeable Z, R, E, and F keys for loading npz data sets.
• Modularize the error and loss calculations.
• Reduce regularization strength to 1e-10 instead of 1e-15.
• Split predict module into models and predictors.
• Install bayesian-optimization from git until scipy bug is fixed.
• Merged sGDML changes up to v0.5.4 (124de3dd8d46a0622bd10c3b4ab033a00dbd3c27).
• Predict times are logged at debug level.
• Ray must be initialized outside of mbePredict class.
• Use n_workers instead of n_cores in mbePredict.

Fixed

• mbe_worker with heterogeneous n-body structures (e.g., solute+solvent).
• Custom todict method for ASE calculator.
  Fixes attached ASE trajectory in reading entity_ids.
• Store ASE Atoms object to avoid recalculating energies and forces in ASE calculator.
• Doc references to respective SchNet functions.

Removed

• Dependency on natsort.
• mbgdml.data.mbModel was adsorbed into mbgdml.models.gdmlModel.
• structureSets and sampling for data sets are no longer supported and subsequently removed.
  This functionality was incorporated into reptar.

v0.0.3

Added

• SchNetPack prediction capabilities.
• GAP prediction capabilities.
• Training loss function that includes a weighted energy RMSE component.
• Require integration constant evaluation option regardless of performance.
• Initial grid for Bayesian optimization to guide sigma_bounds.
• Ability to keep all trained models instead of just the best one.
• Log parallel optimization.
• Plot Gaussian process from hyperparameter Bayesian optimization.
• Plot cluster losses and population histogram using matplotlib.
• Option to use a sequential reduction optimizer for Bayesian optimization.
• Specify Gaussian process keyword arguments for the final iterative training task.

Changed

• Removing md module in favor of having an interfaces module.
• Storage of n-body energies and forces in predict sets.
• Redesigned predict methods and parallelized with ray.
• Included a many-body expansion, mbe, module to handle n-body energy and force predictions.
• Updated API documentation tree.
• Elements logging in tasks and models are condensed (i.e., no spaces).
• Default gp_params for Bayesian optimization.
• MD5 hashes are no longer stored in bytes.
• Do not include training set in any problematic clustering.
  Training structures are not included in dataset clustering or plots.
• Training JSON to training.json instead of log.json.
• Iterative training task directory names to state the training set size.

Fixed

• Added missed torchtools for GDML.
• model0 was not working with iterative training.
• Iterative training would randomly sample every training set.

Removed

• No longer can make many-body dataset with model predictions (with create_mb_from_models).
• e_f_contributions was replaced by the mbe module.

v0.0.2

Added

• Iterative training procedure by finding problematic structures.
• Bayesian optimization for hyperparameter search.
• Basic logging capabilities.
• Write JSON file after training with useful information.
• Specify validation structures when training.

Changed

• Sort md5_data keys for consistency.
• Renamed add_pes_data to add_pes_json
• asdict is now a method instead of a property.
• Removed sGDML dependency.
• Use relative imports.
• Hyperparameter grid search in mbGDMLTrain class.
• Moved sGDML modified training routines to _train.py.
• Changed Rset_md5 to r_prov_ids and Rset_info to r_prov_specs.
• Improved the write_xyz and string_coords functions.
• comp_ids is now a 1D array where the index of the label is the entity_id.

Fixed

• Grammar and typos in documentation.
• Address Sphinx documentation warnings and errors.
• Only deploy documentation on keithgroup repo.
• Correct dataSet Rset_info documentation.

Removed

• qc module. This does not belong in this package.

v0.0.1

Initial release!