Update pypolymlp doc

doc/pypolymlp.md

@@ -64,7 +64,7 @@ in the distribution from GitHub or PyPI.
    `phono3py_params.yaml`. Use {ref}`--cf3 <cf3_option>` and {ref}`--sp
    <sp_option>` option simultaneously.
 4. Develop MLPs. By default, 90 and 10 percents of the dataset are used for the
-   training and test, respectively. At this step `phono3py.pmlp` is saved.
+   training and test, respectively. At this step `polymlp.yaml` is saved.
 5. Generate displacements in supercells either systematic or random displacements.
 6. Evaluate MLPs for forces of the supercells generated in step 5.
 7. Calculate force constants from displacement-force dataset from steps 5 and 6.
@@ -217,7 +217,7 @@ Regression: model selection ...
 - alpha = 1.000e-01 : rmse (train, test) = 0.00002 0.00002
 - alpha = 1.000e+00 : rmse (train, test) = 0.00002 0.00002
 - alpha = 1.000e+01 : rmse (train, test) = 0.00002 0.00002
-MLPs were written into "phono3py.pmlp"
+MLPs were written into "polymlp.yaml"
 ------------------------------ pypolymlp end -------------------------------
 Generate displacements (--rd or -d) for proceeding to phonon calculations.
 Summary of calculation was written in "phono3py.yaml".
@@ -231,7 +231,7 @@ Summary of calculation was written in "phono3py.yaml".
 
 Information about the development of MLPs using pypolymlp is provided between
 the `pypolymlp start` and `pypolymlp end` sections. The polynomial MLPs are
-saved in the `phono3py.pmlp` file, which can be reused in subsequent phono3py
+saved in the `polymlp.yaml` file, which can be reused in subsequent phono3py
 executions with the `--pypolymlp` option when only displacements (and no forces)
 are provided.
 
@@ -240,7 +240,7 @@ are provided.
 
 With the `-d` option, displacements are systematically generated while taking
 crystal symmetry into account. When running with the `--pypolymlp` option, MLPs
-are read from `phono3py.pmlp` if the file exists. In this case, training data is
+are read from `polymlp.yaml` if the file exists. In this case, training data is
 no longer required, and files such as `phono3py.yaml` can be used as the input
 structure file.
 
@@ -274,7 +274,7 @@ NAC parameters were read from "phono3py.yaml".
 Pypolymlp is a generator of polynomial machine learning potentials.
 Please cite the paper: A. Seko, J. Appl. Phys. 133, 011101 (2023).
 Pypolymlp is developed at https://github.com/sekocha/pypolymlp.
-Load MLPs from "phono3py.pmlp".
+Load MLPs from "polymlp.yaml".
 ------------------------------ pypolymlp end -------------------------------
 Generate displacements
   Displacement distance: 0.01
@@ -318,7 +318,7 @@ Random displacements are generated by specifying {ref}`--rd
 <random_displacements_option>` option. To compute force constants with random
 displacements, an external force constants calculator is necessary. By default,
 symfc is used unless another force constants solver is explicitly specified.
-When running with the `--pypolymlp` option, MLPs are read from `phono3py.pmlp`
+When running with the `--pypolymlp` option, MLPs are read from `polymlp.yaml`
 if the file exists. In this case, training data is no longer required, and files
 such as `phono3py.yaml` can be used as the input structure file.
 
@@ -352,7 +352,7 @@ NAC parameters were read from "phono3py.yaml".
 Pypolymlp is a generator of polynomial machine learning potentials.
 Please cite the paper: A. Seko, J. Appl. Phys. 133, 011101 (2023).
 Pypolymlp is developed at https://github.com/sekocha/pypolymlp.
-Load MLPs from "phono3py.pmlp".
+Load MLPs from "polymlp.yaml".
 ------------------------------ pypolymlp end -------------------------------
 Generate random displacements
   Twice of number of snapshots will be generated for plus-minus displacements.
@@ -406,10 +406,10 @@ displacements. Note that to achieve accurate force constants, the actual number
 of generated supercells is twice the specified number. If `--rd` is omitted,
 systematic displacements are introduced.
 
-Once the file `phono3py.pmlp` is obtained, force constants can be calculated
-using MLPs from `phono3py.pmlp`. After removing the `fc3.hdf5` and `fc2.hdf5`
-files, `phono3py-load` will detect `phono3py.pmlp` and then compute the force
-constants by loading the MLPs from `phono3py.pmlp` as follows:
+Once the file `polymlp.yaml` is obtained, force constants can be calculated
+using MLPs from `polymlp.yaml`. After removing the `fc3.hdf5` and `fc2.hdf5`
+files, `phono3py-load` will detect `polymlp.yaml` and then compute the force
+constants by loading the MLPs from `polymlp.yaml` as follows:
 
 ```
 % phono3py-load --pypolymlp --rd 100 --amplitude 0.005 phono3py.yaml
@@ -441,7 +441,7 @@ NAC parameters were read from "phono3py.yaml".
 Pypolymlp is a generator of polynomial machine learning potentials.
 Please cite the paper: A. Seko, J. Appl. Phys. 133, 011101 (2023).
 Pypolymlp is developed at https://github.com/sekocha/pypolymlp.
-Load MLPs from "phono3py.pmlp".
+Load MLPs from "polymlp.yaml".
 ------------------------------ pypolymlp end -------------------------------
 Generate random displacements
   Twice of number of snapshots will be generated for plus-minus displacements.
@@ -542,3 +542,14 @@ the next 100 supercells) will be computed and included. With this procedure in
 mind, it may be convenient to generate a sufficiently large number of supercells
 with random displacements in advance, such as 1000 supercells, before starting
 the LTC calculation with pypolymlp.
+
+## Converting `phono3py.pmlp` to `polymlp.yaml`
+
+In older versions, polynomial MLPs were stored in `phono3py.pmlp`. This file can
+be converted to `polymlp.yaml` using the following Python snippet.
+
+```python
+from pypolymlp.mlp_dev.pypolymlp import Pypolymlp
+polymlp = Pypolymlp()
+polymlp.convert_to_yaml(filename_txt="phono3py.pmlp", filename_yaml="polymlp.yaml”)
+```