remove config_file arg and use pydantic model instead

src/autoplex/auto/rss/flows.py

@@ -1,13 +1,13 @@
 """RSS (random structure searching) flow for exploring and learning potential energy surfaces from scratch."""
 
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 
 from jobflow import Flow, Maker, Response, job
-from monty.serialization import loadfn
 
 from autoplex.auto.rss.jobs import do_rss_iterations, initial_rss
+from autoplex.settings import RssConfig
 
 MODULE_DIR = Path(os.path.dirname(__file__))
 
@@ -21,24 +21,13 @@ class RssMaker(Maker):
     ----------
     name: str
         Name of the flow.
-    config_file: Path | str | None
-        Path to the custom configuration file that defines the setup parameters for the whole RSS workflow.
-        If not provided, the default file 'rss_default_configuration.yaml' will be used.
+    config: RssConfig | None
+        Pydantic model that defines the setup parameters for the whole RSS workflow.
+        If not provided, the defaults from 'autoplex.settings.RssConfig' will be used.
     """
 
     name: str = "ml-driven rss"
-    config_file: Path | str | None = None
-    CONFIG = loadfn(MODULE_DIR / "rss_default_configuration.yaml")
-
-    def __post_init__(self) -> None:
-        """Ensure that custom configuration parameters are loaded when the maker is initialized."""
-        if self.config_file and Path(self.config_file).resolve(strict=True):
-            new_config = loadfn(Path(self.config_file).resolve())
-
-            for key, value in new_config.items():
-                # TODO: Need better defaults in default file or we move to pydantic models
-                if key in self.CONFIG and isinstance(value, type(self.CONFIG[key])):
-                    self.CONFIG[key] = value
+    config: RssConfig | None = field(default_factory=lambda: RssConfig())
 
     @job
     def make(self, **kwargs):
@@ -75,7 +64,7 @@ def make(self, **kwargs):
         buildcell_options: list[dict] | None
             Customized parameters for buildcell. Default is None.
         fragment: Atoms | list[Atoms] | None
-            Fragment(s) for random structures, e.g., molecules, to be placed indivudally intact.
+            Fragment(s) for random structures, e.g., molecules, to be placed individually intact.
             atoms.arrays should have a 'fragment_id' key with unique identifiers for each fragment if in same Atoms.
             atoms.cell must be defined (e.g., Atoms.cell = np.eye(3)*20).
         fragment_numbers: list[str] | None
@@ -245,10 +234,10 @@ def make(self, **kwargs):
             - 'current_iter': int, The current iteration index.
             - 'kb_temp': float, The temperature (in eV) for Boltzmann sampling.
         """
-        self.CONFIG.update(kwargs)
-        self._process_hookean_paras(self.CONFIG)
+        updated_config = self.config.model_copy(update=kwargs)
+        config_params = updated_config.model_dump()
 
-        config_params = self.CONFIG.copy()
+        self._process_hookean_paras(config_params)
 
         if "train_from_scratch" not in config_params:
             raise ValueError(
@@ -345,7 +334,8 @@ def make(self, **kwargs):
 
         return Response(replace=Flow(rss_flow), output=do_rss_job.output)
 
-    def _process_hookean_paras(self, config):
+    @staticmethod
+    def _process_hookean_paras(config):
         if "hookean_paras" in config:
             config["hookean_paras"] = {
                 tuple(map(int, k.strip("()").split(", "))): tuple(v)