Major dependency updates after numpy 2.0.0

[qchempku2017]

Summary

Major changes:

• Update numpy dependency to numpy >= 2.0.0
• Update pymatgen, monty, h5py, scipy, scikit-learn dependencies accordingly.
• Update scip installation procedure as pyscipopt now has bundled solver libarary, no longer needs to install a stand-alone package before testing.

Todos

If this is work in progress, what else needs to be done?

• Update package dependencies
• Test locally
• Test remotely through workflows

Checklist

• All existing tests pass.
• Tests have been added for any new features/fixes.
• Docstrings have been added in the Google docstring format.

Tip: Install pre-commit hooks to auto-check types and linting before every commit:

pip install -U pre-commit
pre-commit install

[qchempku2017]

In order to ensure compatibility across platform, the current approach I take is to enforce all int and long type variables that appears in smol/utils/cluster cython modules to be np.int32_t, while all double types to be np.float64_t. Thus, the functions that use these modules in the Python frontend will be responsible to check and convert into the correct input format before calling the Cython backend.

For example:

https://github.com/qchempku2017/smol/blob/94adb8c836847e95e288f7889fd88395e4c9ae29/smol/utils/cluster/container.pyx#L84C1-L104C21

@lbluque @kamronald There might be a smarter solution. If you have better suggestions please feel free to reach out!

[qchempku2017]

@lbluque Please note that test_cofe/test_expansion/test_prune is not completely safe.

Pruning may occasionally throw out an orbit entirely, causing inconsistency between ints.shape and orbit_multiplicities.shape. I encountered this rare case just now. Fixed by replacing with "expansion.cluster_subspace.orbit_multiplicities".