Make sure that supercell_matrices of single-atom-displaced and rattled supercells are the same per default

src/autoplex/auto/phonons/flows.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import logging
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING
 
@@ -41,6 +42,7 @@
     run_supercells,
 )
 from autoplex.benchmark.phonons.jobs import write_benchmark_metrics
+from autoplex.data.phonons.jobs import reduce_supercell_size_job
 from autoplex.fitting.common.flows import MLIPFitMaker
 
 __all__ = [
@@ -186,7 +188,9 @@ class CompleteDFTvsMLBenchmarkWorkflow(Maker):
     atomwise_regularization_list: list | None = None
     soap_delta_list: list | None = None
     n_sparse_list: list | None = None
-    supercell_settings: dict = field(default_factory=lambda: {"min_length": 15})
+    supercell_settings: dict = field(
+        default_factory=lambda: {"min_length": 15, "max_length": 20}
+    )
     benchmark_kwargs: dict = field(default_factory=dict)
     path_to_default_hyperparameters: Path | str = MLIP_PHONON_DEFAULTS_FILE_PATH
     summary_filename_prefix: str = "results_"
@@ -245,6 +249,8 @@ def make(
             pymatgen structure for benchmarking.
         benchmark_mp_ids: list[str] | None
             Materials Project ID of the benchmarking structure.
+        use_defaults_fitting: bool
+            Use the fit defaults.
         fit_kwargs : dict.
             dict including MLIP fit keyword args.
 
@@ -266,6 +272,27 @@ def make(
         }
 
         for structure, mp_id in zip(structure_list, mp_ids):
+            self.supercell_settings.setdefault(mp_id, {})
+            logging.info(
+                "Currently, "
+                "the same supercell settings for single-atom displaced and rattled supercells are used."
+            )
+            supercell_matrix_job = reduce_supercell_size_job(
+                structure=structure,
+                min_length=self.supercell_settings.get("min_length", 15),
+                max_length=self.supercell_settings.get("max_length", 20),
+                fallback_min_length=self.supercell_settings.get(
+                    "fallback_min_length", 12
+                ),
+                max_atoms=self.supercell_settings.get("max_atoms", 500),
+                min_atoms=self.supercell_settings.get("min_atoms", 50),
+                step_size=self.supercell_settings.get("step_size", 1.0),
+            )
+            flows.append(supercell_matrix_job)
+            self.supercell_settings[mp_id][
+                "supercell_matrix"
+            ] = supercell_matrix_job.output
+
             if self.add_dft_random_struct:
                 add_dft_rand = self.add_dft_random(
                     structure=structure,

src/autoplex/auto/phonons/jobs.py

@@ -335,9 +335,26 @@ def dft_phonopy_gen_data(
     jobs = []
     dft_phonons_output = {}
     dft_phonons_dir_output = []
+    if supercell_settings is None:
+        supercell_settings = field(
+            default_factory=lambda: {"min_length": 15, "max_length": 20}
+        )
     supercell_matrix = supercell_settings.get(mp_id, {}).get("supercell_matrix")
     if not supercell_matrix:
-        supercell_matrix = reduce_supercell_size(structure, **supercell_settings)
+        filtered_settings = {  # mismatching mp_ids would lead to a key error
+            key: value
+            for key, value in supercell_settings.items()
+            if key
+            in [
+                "min_length",
+                "max_length",
+                "fallback_min_length",
+                "max_atoms",
+                "min_atoms",
+                "step_size",
+            ]
+        }
+        supercell_matrix = reduce_supercell_size(structure, **filtered_settings)
 
     if phonon_displacement_maker is None:
         phonon_displacement_maker = TightDFTStaticMaker(name="dft phonon static")

src/autoplex/data/phonons/flows.py

@@ -352,7 +352,9 @@ class RandomStructuresDataGenerator(Maker):
     rattle_seed: int = 42
     rattle_mc_n_iter: int = 10
     w_angle: list[float] | None = None
-    supercell_settings: dict | None = field(default_factory=lambda: {"min_length": 15})
+    supercell_settings: dict | None = field(
+        default_factory=lambda: {"min_length": 15, "max_length": 20}
+    )
 
     def make(
         self,
@@ -378,7 +380,9 @@ def make(
             If None, will default to [0.90, 0.95, 0.98, 0.99, 1.01, 1.02, 1.05, 1.10].
         """
         if self.supercell_settings is None:
-            self.supercell_settings = field(default_factory=lambda: {"min_length": 15})
+            self.supercell_settings = field(
+                default_factory=lambda: {"min_length": 15, "max_length": 20}
+            )
         jobs = []  # initializing empty job list
         outputs = []
 
@@ -733,9 +737,23 @@ def make_from_ml_model(
             self.phonon_displacement_maker,
             self.static_energy_maker,
         ) = ml_prep
+        filtered_settings = {
+            key: value
+            for key, value in supercell_settings.items()
+            if key
+            in [
+                "min_length",
+                "max_length",
+                "fallback_min_length",
+                "max_atoms",
+                "min_atoms",
+                "step_size",
+            ]
+        }
         supercell_matrix = reduce_supercell_size(
-            structure=structure, **supercell_settings
+            structure=structure, **filtered_settings
         )
+
         flow = self.make(
             structure=structure, supercell_matrix=supercell_matrix, **make_kwargs
         )

src/autoplex/data/phonons/jobs.py

@@ -16,7 +16,7 @@
 def reduce_supercell_size_job(
     structure: Structure,
     min_length: float = 18,
-    max_length: float = 22,
+    max_length: float = 20,
     fallback_min_length: float = 12,
     min_atoms: int = 100,
     max_atoms: int = 500,

src/autoplex/fitting/common/mlip-phonon-defaults.json

@@ -10,7 +10,8 @@
       "do_copy_at_file": "F",
       "openmp_chunk_size": 10000,
       "gp_file": "gap_file.xml",
-      "two_body": true,
+      "e0_offset": 0.0,
+      "two_body": false,
       "three_body": false,
       "soap": true
     },
@@ -45,10 +46,10 @@
       "atom_sigma": 0.5,
       "zeta": 4,
       "cutoff": 5.0,
-      "cutoff_transition_width": 0.5,
+      "cutoff_transition_width": 1.0,
       "central_weight": 1.0,
       "n_sparse": 6000,
-      "delta": 0.50,
+      "delta": 1.00,
       "f0": 0.0,
       "covariance_type": "dot_product",
       "sparse_method": "cur_points"