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- First 1.0.0 release to reflect the maturity of the matgl code! All changes below are the efforts of @kenko911.
- Equivariant TensorNet and SO3Net are now implemented in MatGL.
- Refactoring of M3GNetCalculator and M3GNetDataset into generic PESCalculator and MGLDataset for use with all models instead of just M3GNet.
- Training framework has been unified for all models.
- ZBL repulsive potentials has been implemented.
- Added Tensor Placement Calls For Ease of Training with PyTorch Lightning (@melo-gonzo).
- Allow extraction of intermediate outputs in "embedding", "gc_1", "gc_2", "gc_3", and "readout" layers for use as atom, bond, and structure features. (@JiQi535)
- Update Potential version numbers.
- set pbc_offsift and pos as float64 by @lbluque in materialsvirtuallab#153
- Bump pytorch-lightning from 2.0.7 to 2.0.8 by @dependabot in materialsvirtuallab#155
- add cpu() to avoid crash when using ase with GPU by @kenko911 in materialsvirtuallab#156
- Added the united test for hessian in test_ase.py to improve the coverage score by @kenko911 in materialsvirtuallab#157
- AtomRef Updates by @lbluque in materialsvirtuallab#158
- Bump pymatgen from 2023.8.10 to 2023.9.2 by @dependabot in materialsvirtuallab#160
- Remove torch.unique for finding the maximum three body index and little cleanup in united tests by @kenko911 in materialsvirtuallab#161
- Bump pymatgen from 2023.9.2 to 2023.9.10 by @dependabot in materialsvirtuallab#162
- Add united test for trainer.test and description in the example by @kenko911 in materialsvirtuallab#165
- Bump pytorch-lightning from 2.0.8 to 2.0.9 by @dependabot in materialsvirtuallab#167
- Sequence instead of list for inputs by @lbluque in materialsvirtuallab#169
- Avoiding crashes for PES training without stresses and update pretrained models by @kenko911 in materialsvirtuallab#168
- Bump pymatgen from 2023.9.10 to 2023.9.25 by @dependabot in materialsvirtuallab#173
- Allow to choose distribution in xavier_init by @lbluque in materialsvirtuallab#174
- An example for the simple training of M3GNet formation energy model is added by @kenko911 in materialsvirtuallab#176
- Directed line graph by @lbluque in materialsvirtuallab#178
- Bump pymatgen from 2023.9.25 to 2023.10.4 by @dependabot in materialsvirtuallab#180
- Bump torch from 2.0.1 to 2.1.0 by @dependabot in materialsvirtuallab#181
- Bump pymatgen from 2023.10.4 to 2023.10.11 by @dependabot in materialsvirtuallab#183
- add testing to m3gnet potential training example by @lbluque in materialsvirtuallab#179
- Update Training a MEGNet Formation Energy Model with PyTorch Lightnin… by @1152041831 in materialsvirtuallab#185
- Bump pymatgen from 2023.10.11 to 2023.11.12 by @dependabot in materialsvirtuallab#187
- dEdLat contribution for stress calculations is added and Universal Potentials are updated by @kenko911 in materialsvirtuallab#189
- Bump torch from 2.1.0 to 2.1.1 by @dependabot in materialsvirtuallab#190
- @1152041831 made their first contribution in materialsvirtuallab#185
Full Changelog: https://github.com/materialsvirtuallab/matgl/compare/v0.8.5...v0.8.6
- Extend the functionality of ASE-interface for molecular systems and include more different ensembles. (@kenko911)
- Improve the dgl graph construction and fix the if statements for stress and atomwise training. (@kenko911)
- Refactored MEGNetDataset and M3GNetDataset classes with optimizations.
- Bug fix for np.meshgrid. (@kenko911)
- Add site-wise predictions for Potential. (@lbluque)
- Enable CLI tool to be used for multi-fidelity models. (@kenko911)
- Minor fix for model version for DIRECT model.
- Fixed bug with loading of models trained with GPUs.
- Updated default model for relaxations to be the
M3GNet-MP-2021.2.8-DIRECT-PES model.
- Fix a bug with use of set2set in M3Gnet implementation that affected intensive models such as the formation energy model. M3GNet model version is updated to 2 to invalidate previous models. Note that PES models are unaffected. (@kenko911)
- Minor optimizations for memory and isolated atom training (@kenko911)
- MatGL now supports structures with isolated atoms. (@JiQi535)
- Fourier expansion layer and generalize cutoff polynomial. (@lbluque)
- Radial bessel (zeroth order bessel). (@lbluque)
- Simple CLI tool
mgladded.
- Bug fix for training loss_fn.
- Refactoring of training utilities. Added example for training an M3GNet potential.
- Minor internal refactoring of basis expansions into
_basis.py. (@lbluque)
- Critical bug fix for code regression affecting pre-loaded models.
- M3GNet Formation energy model added, with example notebook.
- M3GNet.predict_structure method added.
- Massively improved documentation at http://matgl.ai.
- Minor doc and code usability improvements.
- Minor improvements to model versioning scheme.
- Added
matgl.get_available_pretrained_models()to help with model discovery. - Misc doc and error message improvements.
- Model versioning scheme implemented.
- Added convenience method to clear cache.
- Model serialization has been completely rewritten to make it easier to use models out of the box.
- Convenience method
matgl.load_modelis now the default way to load models. - Added a TransformedTargetModel.
- Enable serialization of Potential.
- IMPORTANT: Pre-trained models have been reserialized. These models can only be used with v0.5.0+!
- Pre-trained M3GNet universal potential
- Pytorch lightning training utility.
- Major refactoring of MEGNet and M3GNet models and organization of internal implementations. Only key API are exposed via matgl.models or matgl.layers to hide internal implementations (which may change).
- Pre-trained models ported over to new implementation.
- Model download now implemented.
- Fixes for pre-trained model download.
- Speed up M3GNet 3-body computations.
- Pre-trained MEGNet models for formation energies and band gaps are now available.
- MEGNet model implemented with
predict_structureconvenience method. - Example notebook demonstrating pre-trained model usage is available.
- Initial working version with m3gnet and megnet.