change status

docs/source/user_guide/calculations.rst

@@ -27,7 +27,7 @@ Below is a usage example with the minimum required parameters. These parameters
 
 .. code-block:: python
 
-    SinglePointCalculation = CalculationFactory("janus.sp")
+    SinglePointCalculation = CalculationFactory("mlip.sp")
     submit(SinglePointCalculation, code=InstalledCode, structure=StructureData, metadata={"options": {"resources": {"num_machines": 1}}})
 
 The inputs can be grouped into a dictionary:
@@ -43,7 +43,7 @@ The inputs can be grouped into a dictionary:
             "precision": Str,
             "device": Str,
         }
-    SinglePointCalculation = CalculationFactory("janus.sp")
+    SinglePointCalculation = CalculationFactory("mlip.sp")
     submit(SinglePointCalculation, **inputs)
 
 
@@ -72,7 +72,7 @@ And it is used as shown below. Note that some parameters, which are specific to
     config = JanusConfigfile("path/to/config.yaml")
 
     # Define calculation to run
-    SinglePointCalculation = CalculationFactory("janus.sp")
+    SinglePointCalculation = CalculationFactory("mlip.sp")
 
     # Run calculation
     result, node = run_get_node(
@@ -140,7 +140,7 @@ Below is a usage example with some additional geometry optimisation parameters.
 .. code-block:: python
 
 
-    GeomOptCalculation = CalculationFactory("janus.opt")
+    GeomOptCalculation = CalculationFactory("mlip.opt")
     submit(GeomOptCalculation, code=InstalledCode, structure=StructureData, max_force=Float(0.1), vectors_only=Bool(True))
 
 
@@ -177,7 +177,7 @@ Below is a usage example with some additional geometry optimisation parameters.
 .. code-block:: python
 
 
-    MDCalculation = CalculationFactory("janus.md")
+    MDCalculation = CalculationFactory("mlip.md")
     submit(MDCalculation, code=InstalledCode, structure=StructureData, ensemble=Str("nve"), md_dict=Dict({'temp':300,'steps': 4,'traj-every':3,'stats-every':1}))
 
 .. note::

docs/source/user_guide/training.rst

@@ -12,7 +12,7 @@ Below is a usage example with some additional training parameters. These paramet
 
 .. code-block:: python
 
-    TrainCalculation = CalculationFactory("janus.train")
+    TrainCalculation = CalculationFactory("mlip.train")
     submit(TrainCalculation, code=InstalledCode, mlip_config=JanusConfigfile, metadata=Dict({'options': {'output_filename': 'aiida-stdout.txt'}}))
 
 
@@ -90,7 +90,7 @@ In that case some additional parameters must be used: foundation_model and fine_
          foundation_model=ModelData
          }
 
-    TrainCalculation = CalculationFactory("janus.train")
+    TrainCalculation = CalculationFactory("mlip.train")
     submit(TrainCalculation,inputs)
 
 A model to fine-tune has to be provided as an input, either as a `ModelData` type (in which case it has to be a model file), or in the config file at the keyword `foundation_model`.

docs/source/user_guide/tutorial.rst

@@ -75,9 +75,9 @@ The calculation must be set:
 .. code-block:: python
 
     from aiida.plugins import CalculationFactory
-    geomoptCalculation = CalculationFactory("janus.opt")
+    geomoptCalculation = CalculationFactory("mlip.opt")
 
-In this case, since we are running a geometry optimisation, the entry point for the calculation is `janus.opt`. For a single point calculation, the entry point would be `janus.sp`.
+In this case, since we are running a geometry optimisation, the entry point for the calculation is `mlip.opt`. For a single point calculation, the entry point would be `mlip.sp`.
 
 Finally, run the calculation:
 
@@ -129,7 +129,7 @@ while `node` is the node of the calculation
     Out : aiida.orm.nodes.process.calculation.calcjob.CalcJobNode
 
     In : print(node)
-    Out: uuid: 1d46ad08-2ea7-4892-9dd6-0240b9aeda8b (pk: 1130) (aiida.calculations:janus.opt)
+    Out: uuid: 1d46ad08-2ea7-4892-9dd6-0240b9aeda8b (pk: 1130) (aiida.calculations:mlip.opt)
 
 
 The calculation can also be interacted with through verdi cli. Use `verdi process list` to show the list of calculations.

pyproject.toml

@@ -3,10 +3,10 @@ name = "aiida-mlip"
 version = "0.2.0"
 description = "machine learning interatomic potentials aiida plugin"
 authors = [
-    "Federica Zanca",
+    "Federica Zanca <[email protected]>",
     "Elliott Kasoar",
     "Jacob Wilkins",
-    "Alin M. Elena",
+    "Alin M. Elena"
 ]
 readme = "README.md"
 packages = [{include = "aiida_mlip"}]
@@ -15,7 +15,7 @@ classifiers = [
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: BSD License",
     "Natural Language :: English",
-    "Development Status :: 3 - Alpha",
+    "Development Status :: 5 - Production/Stable",
     "Framework :: AiiDA"
 ]
 keywords = ["aiida", "plugin"]
@@ -30,10 +30,10 @@ python = "^3.9"
 aiida-core = "^2.5"
 ase = "^3.22.1"
 voluptuous = "^0.14"
+janus-core = "^v0.5.0b0"
 
 [tool.poetry.group.dev.dependencies]
 coverage = {extras = ["toml"], version = "^7.4.1"}
-janus-core = "^v0.4.0b0"
 pgtest = "^1.3.2"
 pytest = "^8.0"
 pytest-cov = "^4.1.0"