diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index ddaeaa13..97d8032a 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/pypolymlp.doctree b/.doctrees/pypolymlp.doctree index aac986ca..da264af5 100644 Binary files a/.doctrees/pypolymlp.doctree and b/.doctrees/pypolymlp.doctree differ diff --git a/_sources/pypolymlp.md b/_sources/pypolymlp.md index 4a1bd73d..00403198 100644 --- a/_sources/pypolymlp.md +++ b/_sources/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 A. Togo and A. Seko, J. Chem. Phys. -**160**, 211001 (2024) [[doi](https://doi.org/10.1063/5.0211296)]. +For further details on combining phono3py calculations with pypolymlp, refer to +A. Togo and A. Seko, J. Chem. Phys. **160**, 211001 (2024) +[[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 ` option. To compute force constants @@ -329,6 +331,14 @@ 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 with respect to the number of supercells in the training +dataset. Lattice thermal conductivity may be a convenient property to monitor +when assessing convergence. + ## Parameters for developing MLPs A few parameters can be specified using the `--mlp-params` option for the diff --git a/pypolymlp.html b/pypolymlp.html index 777fc8ec..f7151c11 100644 --- a/pypolymlp.html +++ b/pypolymlp.html @@ -324,13 +324,14 @@

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