docs: update DPA-2 citation #4483
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Signed-off-by: Jinzhe Zeng <[email protected]>
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📝 Walkthrough📝 WalkthroughWalkthroughThis pull request updates references to a scientific publication by Zhang et al. in multiple documentation files. The changes primarily involve updating a BibTeX citation from a preprint (arXiv) to a published article in "npj Comput. Mater." The modification includes updating the publication year from 2023 to 2024, changing the title, adding volume and page numbers, and updating the DOI. These changes are reflected consistently across the project's documentation files. Changes
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🧰 Additional context used🪛 LanguageTooldoc/train/multi-task-training.md[misspelling] ~29-~29: This word is normally spelled as one. (EN_COMPOUNDS_MULTI_TASK) doc/train/finetuning.md[misspelling] ~97-~97: This word is normally spelled as one. (EN_COMPOUNDS_MULTI_TASK) 🔇 Additional comments (3)deepmd/dpmodel/descriptor/dpa2.py (1)Line range hint The citation for the DPA-2 descriptor has been properly added to the docstring, following standard scientific citation format with all necessary details including authors, title, journal, volume, page numbers, and DOI. doc/train/multi-task-training.md (1)
The DPA-2 citation has been properly updated with:
🧰 Tools🪛 LanguageTool[misspelling] ~29-~29: This word is normally spelled as one. (EN_COMPOUNDS_MULTI_TASK) doc/train/finetuning.md (1)
The DOI link to the DPA2 paper has been correctly updated to match the citation in multi-task-training.md. 🧰 Tools🪛 LanguageTool[misspelling] ~97-~97: This word is normally spelled as one. (EN_COMPOUNDS_MULTI_TASK) Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media? 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
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CITATIONS.bib (1)
145-145: Fix journal name formatting for consistencyThe journal name "npj Comput. Mater" should be formatted consistently across citations. In the same file, another entry uses "Npj Comput. Mater" (see line with Zhang_NpjComputMater_2024_v10_p94).
- journal = {npj Comput. Mater}, + journal = {Npj Comput. Mater},doc/model/dpa2.md (1)
7-7: Improve link formatting using Markdown reference-style linksInstead of using a bare URL, consider using Markdown reference-style links for better readability and maintainability.
- The DPA-2 model implementation. See https://doi.org/10.1038/s41524-024-01493-2 for more details. + The DPA-2 model implementation. See [the published paper][dpa2-paper] for more details. + + [dpa2-paper]: https://doi.org/10.1038/s41524-024-01493-2🧰 Tools
🪛 Markdownlint (0.37.0)
7-7: null
Bare URL used(MD034, no-bare-urls)
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CITATIONS.bib(1 hunks)doc/credits.rst(1 hunks)doc/model/dpa2.md(1 hunks)
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- doc/credits.rst
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🪛 Markdownlint (0.37.0)
doc/model/dpa2.md
7-7: null
Bare URL used
(MD034, no-bare-urls)
🔇 Additional comments (1)
CITATIONS.bib (1)
131-150: Citation update looks good!
The BibTeX entry has been properly updated from preprint to published article with all necessary fields (journal, volume, number, pages, DOI) correctly formatted.
iProzd
reviewed
Dec 21, 2024
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4483 +/- ##
==========================================
- Coverage 84.41% 84.41% -0.01%
==========================================
Files 670 670
Lines 62149 62148 -1
Branches 3487 3486 -1
==========================================
- Hits 52465 52464 -1
+ Misses 8558 8557 -1
- Partials 1126 1127 +1 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
iProzd
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Dec 21, 2024
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Dec 22, 2024
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<!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Updated references in the bibliography for the DPA-2 model to include a new article entry for 2024. - Added a new reference for an attention-based descriptor. - **Bug Fixes** - Corrected reference links in documentation to point to updated DOI links instead of arXiv. - **Documentation** - Revised entries in the credits and model documentation to reflect the latest citations and details. - Enhanced clarity and detail in fine-tuning documentation for TensorFlow and PyTorch implementations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> (cherry picked from commit deaeec9)
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Dec 23, 2024
<!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Updated references in the bibliography for the DPA-2 model to include a new article entry for 2024. - Added a new reference for an attention-based descriptor. - **Bug Fixes** - Corrected reference links in documentation to point to updated DOI links instead of arXiv. - **Documentation** - Revised entries in the credits and model documentation to reflect the latest citations and details. - Enhanced clarity and detail in fine-tuning documentation for TensorFlow and PyTorch implementations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> (cherry picked from commit deaeec9)
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* change property.npy to any name * Init branch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change | to Union * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change sub_var_name default to [] * Solve pre-commit * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * solve scanning github * fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete useless file * Solve some UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve precommit * slove pre * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve dptest UT, dpatomicmodel UT, code scannisang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete param and * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve UT fail caused by task_dim and property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix permutation error * Add property bias UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover rcond doc * recover blank * Change code according according to coderabbitai * solve pre-commit * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change apply_bias doc * update the version compatibility * feat (tf/pt): add atomic weights to tensor loss (deepmodeling#4466) Interfaces are of particular interest in many studies. However, the configurations in the training set to represent the interface normally also include large parts of the bulk material. As a result, the final model would prefer the bulk information while the interfacial information is less learnt. It is difficult to simply improve the proportion of interfaces in the configurations since the electronic structures of the interface might only be reasonable with a certain thickness of bulk materials. Therefore, I wonder whether it is possible to define weights for atomic quantities in loss functions. This allows us to add higher weights for the atomic information for the regions of interest and probably makes the model "more focused" on the region of interest. In this PR, I add the keyword `enable_atomic_weight` to the loss function of the tensor model. In principle, it could be generalised to any atomic quantity, e.g., atomic forces. I would like to know the developers' comments/suggestions about this feature. I can add support for other loss functions and finish unit tests once we agree on this feature. Best. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced an optional parameter for atomic weights in loss calculations, enhancing flexibility in the `TensorLoss` class. - Added a suite of unit tests for the `TensorLoss` functionality, ensuring consistency between TensorFlow and PyTorch implementations. - **Bug Fixes** - Updated logic for local loss calculations to ensure correct application of atomic weights based on user input. - **Documentation** - Improved clarity of documentation for several function arguments, including the addition of a new argument related to atomic weights. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * delete sub_var_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover to property key * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix conflict * Fix UT * Add document of property fitting * Delete checkpoint * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add get_property_name to DeepEvalBackend * pd: fix learning rate setting when resume (deepmodeling#4480) "When resuming training, there is no need to add `self.start_step` to the step count because Paddle uses `lr_sche.last_epoch` as the input for `step`, which already records the `start_step` steps." learning rate are correct after fixing  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced training process with improved optimizer configuration and learning rate adjustments. - Refined logging of training and validation results for clarity. - Improved model saving logic to preserve the latest state during interruptions. - Enhanced tensorboard logging for detailed tracking of training metrics. - **Bug Fixes** - Corrected lambda function for learning rate scheduler to reference warmup steps accurately. - **Chores** - Streamlined data loading and handling for efficient training across different tasks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * docs: update deepmd-gnn URL (deepmodeling#4482) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated guidelines for creating and integrating new models in the DeePMD-kit framework. - Added new sections on descriptors, fitting networks, and model requirements. - Enhanced unit testing section with instructions for regression tests. - Updated URL for the DeePMD-GNN plugin to reflect new repository location. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * docs: update DPA-2 citation (deepmodeling#4483) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Updated references in the bibliography for the DPA-2 model to include a new article entry for 2024. - Added a new reference for an attention-based descriptor. - **Bug Fixes** - Corrected reference links in documentation to point to updated DOI links instead of arXiv. - **Documentation** - Revised entries in the credits and model documentation to reflect the latest citations and details. - Enhanced clarity and detail in fine-tuning documentation for TensorFlow and PyTorch implementations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> * docs: fix a minor typo on the title of `install-from-c-library.md` (deepmodeling#4484) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated formatting of the installation guide for the pre-compiled C library. - Icons for TensorFlow and JAX are now displayed together in the header. - Retained all installation instructions and compatibility notes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * fix: print dlerror if dlopen fails (deepmodeling#4485) xref: njzjz/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * change doc to py * Add out_bias out_std doc * change bias method to compute_stats_do_not_distinguish_types * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change var_name to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change logic of extensive bias * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add doc for neww added parameter * change doc for compute_stats_do_not_distinguish_types * try to fix dptest * change all property to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete key 'property' completely * Fix UT * Fix dptest UT * pd: fix oom error (deepmodeling#4493) Paddle use `MemoryError` rather than `RuntimeError` used in pytorch, now I can test DPA-1 and DPA-2 in 16G V100...  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Improved detection of out-of-memory (OOM) errors to enhance application stability. - Ensured cached memory is cleared upon OOM errors, preventing potential memory leaks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * pd: add missing `dp.eval()` in pd backend (deepmodeling#4488) Switch to eval mode when evaluating model, otherwise `self.training` will be `True`, backward graph will be created and cause OOM <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced model evaluation state management to ensure correct behavior during evaluation. - **Bug Fixes** - Improved type consistency in the `normalize_coord` function for better computational accuracy. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * [pre-commit.ci] pre-commit autoupdate (deepmodeling#4497) <!--pre-commit.ci start--> updates: - [github.com/astral-sh/ruff-pre-commit: v0.8.3 → v0.8.4](astral-sh/ruff-pre-commit@v0.8.3...v0.8.4) <!--pre-commit.ci end--> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Delete attribute * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve comment * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete property_name in serialize --------- Signed-off-by: Jinzhe Zeng <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Chenqqian Zhang <[email protected]> Co-authored-by: Jia-Xin Zhu <[email protected]> Co-authored-by: HydrogenSulfate <[email protected]> Co-authored-by: Jinzhe Zeng <[email protected]>
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New Features
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