Update pypolymlp doc

doc/pypolymlp.md

@@ -15,13 +15,15 @@ The training process involves using a dataset consisting of supercell
 displacements, forces, and energies. The trained MLPs are then employed to
 compute forces for supercells with specific displacements.
 
-For more details on the methodology, refer to <u>A. Togo and A. Seko, J. Chem. Phys.
-**160**, 211001 (2024)</u> [[doi](https://doi.org/10.1063/5.0211296)].
+For further details on combining phono3py calculations with pypolymlp, refer to
+<u>A. Togo and A. Seko, J. Chem. Phys. **160**, 211001 (2024)</u>
+[[doi](https://doi.org/10.1063/5.0211296)]
+[[arxiv](https://arxiv.org/abs/2401.17531)].
 
 An example of its usage can be found in the `example/NaCl-pypolymlp` directory
 in the distribution from GitHub or PyPI.
 
-## Requirement
+## Requirements
 
 - [pypolymlp](https://github.com/sekocha/pypolymlp)
 - [symfc](https://github.com/symfc/symfc)
@@ -228,7 +230,7 @@ displacement distance of 0.001 Angstrom. The forces for these supercells are
 then evaluated using pypolymlp. Both the generated displacements and the
 corresponding forces are stored in the `phono3py_mlp_eval_dataset` file.
 
-### Steps 4-6: Force constants calculation (random displacements in step 5)
+### Steps 4-7: Force constants calculation (random displacements in step 5)
 
 After developing MLPs, random displacements are generated by specifying
 {ref}`--rd <random_displacements_option>` option. To compute force constants
@@ -329,6 +331,10 @@ an additional 200 supercells. In total, 400 supercells are created. The forces
 for these supercells are then evaluated. Finally, the force constants are
 calculated using symfc.
 
+## Convergence with respect to dataset size
+
+In general, increasing the amount of data improves the accuracy of representing force constants. Therefore, it is recommended to check the convergence of the target property as the dataset size grows. Lattice thermal conductivity is a convenient property to monitor when assessing convergence with respect to the number of supercells in the training dataset.
+
 ## Parameters for developing MLPs
 
 A few parameters can be specified using the `--mlp-params` option for the

phono3py/file_IO.py

@@ -413,6 +413,26 @@ def read_fc2_from_hdf5(filename="fc2.hdf5", p2s_map=None):
     )
 
 
+def write_datasets_to_hdf5(
+    dataset: dict,
+    phonon_dataset: dict = None,
+    filename: str = "datasets.hdf5",
+    compression: str = "gzip",
+):
+    """Write dataset and phonon_dataset in datasets.hdf5."""
+
+    def _write_dataset(w, dataset: dict, group_name: str):
+        dataset_w = w.create_group(group_name)
+        for key in dataset:
+            dataset_w.create_dataset(key, data=dataset[key], compression=compression)
+
+    with h5py.File(filename, "w") as w:
+        w.create_dataset("version", data=np.bytes_(__version__))
+        _write_dataset(w, dataset, "dataset")
+        if phonon_dataset:
+            _write_dataset(w, phonon_dataset, "phonon_dataset")
+
+
 def write_grid_address_to_hdf5(
     grid_address,
     mesh,