Bump deepmd-kit[cpu] from 3.1.2 to 3.1.3

[dependabot]

Bumps deepmd-kit[cpu] from 3.1.2 to 3.1.3.

Release notes

Sourced from deepmd-kit[cpu]'s releases.

v3.1.3

Highlights

This release focuses on two major themes: easier access to pretrained models and the next stage of the PyTorch roadmap. DeePMD-kit can now download built-in pretrained models directly, and the same release series also introduces a new pretrained model, DPA3-Omol-Large, on top of that mechanism. In parallel, we have started building an experimental exportable PyTorch backend based on the Array API, torch.export, and torch.compile, motivated in part by the deprecation of torch.jit.

Beyond these headline items, v3.1.3 expands PyTorch training capabilities with new optimizers and distributed-training support, improves diagnostics and training safety, adds charge-spin and spin-virial related functionality, and continues to strengthen documentation, CI, packaging, and backend consistency across the project.

Try DPA3-Omol-Large in 3 steps:

# Install the latest version of DeePMD-kit (will be available a few days after this release)
curl -fsSL https://dp1s.deepmodeling.com | bash
# Restart the shell, and download the pretrained model
dp pretrained download DPA3-Omol-Large
# Evaluate your training/test data with the pretrained model
dp test -m ~/.cache/deepmd/pretrained/models/DPA3-Omol-Large.pt -s path_to_your_system

Breaking Changes

• Drop Python 3.9 by @​njzjz in #5078
• Stop providing CUDA 11 pre-built wheels by @​njzjz in #5080
• Prepare to deprecate the devel branch by @​njzjz in #5122

New Features

Pretrained models and model distribution

• Add a built-in pretrained model downloader and alias backend by @​njzjz-bot in #5277
• Add DPA-2.4-7M to the pretrained model registry by @​njzjz-bot in #5307
• Add DPA3-Omol-Large by @​njzjz-bot in #5327

Experimental PyTorch backend

• Add PyTorch support to Array API utilities by @​Copilot in #5198
• Add a new exportable PyTorch backend by @​wanghan-iapcm in #5194
• Provide infrastructure for converting dpmodel classes to PyTorch modules by @​wanghan-iapcm in #5204
• Implement se_t and se_t_tebd descriptors in the experimental PyTorch backend by @​wanghan-iapcm in #5208
• Add energy fitting in the experimental PyTorch backend by @​wanghan-iapcm in #5218
• Add the atomic model in the experimental PyTorch backend by @​wanghan-iapcm in #5220
• Add the full model in the experimental PyTorch backend by @​wanghan-iapcm in #5244
• Auto-generate forward / forward_lower in the torch_module decorator by @​Copilot in #5246
• Add dpa1, dpa2, dpa3, and hybrid descriptors in the experimental PyTorch backend by @​wanghan-iapcm in #5248
• Add DOS, dipole, polar, and property fittings in the experimental PyTorch backend by @​wanghan-iapcm in #5254
• Add dipole, polar, DOS, property, and DP-ZBL models with cross-backend consistency tests by @​wanghan-iapcm in #5260
• Add training infrastructure for the experimental PyTorch backend by @​wanghan-iapcm in #5270
• Implement the .pte inference pipeline with dynamic shapes by @​wanghan-iapcm in #5284
• Implement the energy Hessian model in the experimental PyTorch backend by @​wanghan-iapcm in #5287
• Add DP freeze support and dp test coverage for .pte models by @​wanghan-iapcm in #5302
• Add frozen-model support in the experimental PyTorch backend by @​wanghan-iapcm in #5318

... (truncated)

Commits

• b2c8511 feat(pretrained): add DPA3-Omol-Large (#5327)
• 1ce26cf refactor(pt): fully refactor of HybridMuon optimizer (#5275)
• 9ed2d48 feat(pt_expt): add frozen model (#5318)
• 08394be fix(dpmodel): fix natoms[0] bug, einsum, and return type in EnergyLoss (#5325)
• a9bbf74 fix: nan error when all fparam/aparam have equal values (#5321)
• 3ab3779 feat(pt_expt): add dp freeze support and dp test tests for .pte models (#5302)
• b2805fb [pre-commit.ci] pre-commit autoupdate (#5320)
• 87b1fff chore(pre-commit): remove flake8 hook (#5324)
• 4ce5c73 fix: statisitcs -> statistics (#5315)
• 09345bf fix(lmp): default fparam support (#5311)
• Additional commits viewable in compare view

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