New config

apax/cli/templates/md_config_minimal.yaml

@@ -11,9 +11,9 @@ ensemble:
     tau: 100
 
 duration: <DURATION> # fs
-n_inner: 100 # compiled innner steps
+n_inner: 500 # compiled innner steps
 sampling_rate: 10  # dump interval
-buffer_size: 100
+buffer_size: 2500
 dr_threshold: 0.5 # Neighborlist skin
 extra_capacity: 0
 

apax/cli/templates/train_config_full.yaml

@@ -1,7 +1,6 @@
 n_epochs: <NUMBER OF EPOCHS>
 seed: 1
 patience: null
-n_jitted_steps: 1
 data_parallel: True
 weight_average: null
 
@@ -24,7 +23,7 @@ data:
   n_train: 1000
   n_valid: 100
 
-  batch_size: 32
+  batch_size: 4
   valid_batch_size: 100
 
   shift_method: "per_element_regression_shift"
@@ -39,30 +38,37 @@ data:
 model:
   name: gmnn
   basis:
-    name: gaussian
-    n_basis: 7
-    r_max: 6.0
-    r_min: 0.5
+    name: bessel
+    n_basis: 16
+    r_max: 5.0
 
   ensemble: null
-  # if you would like to train model ensembles, this can be achieved with
-  # the following example.
+  # if you would like to use emirical repulsion corrections
+  # with the following example.
+  # empirical_corrections:
+  #   - name: exponential
+  #     r_max: 1.5
+
+  # if you would like to train model ensembles, this can be
+  # achieved with the following example.
+  # Hint: loss type hase to be changed to a probabalistic loss like nll or crps
   # ensemble:
-  #  kind: full
+  #  kind: shallow
   #  n_members: N
 
   n_radial: 5
   n_contr: 8
-  nn: [512, 512]
+  nn: [256, 256]
 
-  calc_stress: true
+  calc_stress: false
 
-  w_init: normal
+  w_init: lecun
   b_init: zeros
-  descriptor_dtype: fp64
+  descriptor_dtype: fp32
   readout_dtype: fp32
-  scale_shift_dtype: fp32
+  scale_shift_dtype: fp64
   emb_init: uniform
+  use_ntk: false
 
 loss:
 - name: energy
@@ -86,20 +92,21 @@ metrics:
 optimizer:
   name: adam
   kwargs: {}
-  emb_lr: 0.03
-  nn_lr: 0.03
-  scale_lr: 0.001
-  shift_lr: 0.05
-  zbl_lr: 0.001
+  emb_lr: 0.001
+  nn_lr: 0.001
+  scale_lr: 0.0001
+  shift_lr: 0.003
+  zbl_lr: 0.0001
   schedule:
-    name: linear
-    transition_begin: 0
-    end_value: 1e-6
+    name: cyclic_cosine
+    period: 40
+    decay_factor: 0.93
+
 callbacks:
 - name: csv
 
 checkpoints:
-  ckpt_interval: 1
+  ckpt_interval: 500
   # The options below are used for transfer learning
   base_model_checkpoint: null
   reset_layers: []

apax/cli/templates/train_config_minimal.yaml

@@ -8,7 +8,7 @@ data:
 
   n_train: 1000
   n_valid: 100
-  batch_size: 32
+  batch_size: 4
   valid_batch_size: 100
 
 metrics:

apax/config/lr_config.py

@@ -33,13 +33,13 @@ class CyclicCosineLR(LRSchedule, frozen=True, extra="forbid"):
 
     Parameters
     ----------
-    period: int = 20
+    period: int = 40
         Length of a cycle in epochs.
-    decay_factor: NonNegativeFloat = 1.0
+    decay_factor: NonNegativeFloat = 0.93
         Factor by which to decrease the LR after each cycle.
         1.0 means no decrease.
     """
 
     name: Literal["cyclic_cosine"]
-    period: int = 20
-    decay_factor: NonNegativeFloat = 1.0
+    period: int = 40
+    decay_factor: NonNegativeFloat = 0.93

apax/config/md_config.py

@@ -229,12 +229,12 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
         | Time step in fs.
     duration : float, required
         | Total simulation time in fs.
-    n_inner : int, default = 100
+    n_inner : int, default = 500
         | Number of compiled simulation steps (i.e. number of iterations of the
         | `jax.lax.fori_loop` loop). Also determines atoms buffer size.
     sampling_rate : int, default = 10
         | Interval between saving frames.
-    buffer_size : int, default = 100
+    buffer_size : int, default = 2500
         | Number of collected frames to be dumped at once.
     dr_threshold : float, default = 0.5
         | Skin of the neighborlist.
@@ -273,9 +273,9 @@ class MDConfig(BaseModel, frozen=True, extra="forbid"):
     )
 
     duration: PositiveFloat
-    n_inner: PositiveInt = 100
+    n_inner: PositiveInt = 500
     sampling_rate: PositiveInt = 10
-    buffer_size: PositiveInt = 100
+    buffer_size: PositiveInt = 2500
     dr_threshold: PositiveFloat = 0.5
     extra_capacity: NonNegativeInt = 0
 

apax/config/model_config.py

@@ -35,15 +35,15 @@ class BesselBasisConfig(BaseModel, extra="forbid"):
 
     Parameters
     ----------
-    n_basis : PositiveInt, default = 7
+    n_basis : PositiveInt, default = 16
         Number of uncontracted basis functions.
-    r_max : PositiveFloat, default = 6.0
+    r_max : PositiveFloat, default = 5.0
         Cutoff radius of the descriptor.
     """
 
     name: Literal["bessel"] = "bessel"
-    n_basis: PositiveInt = 7
-    r_max: PositiveFloat = 6.0
+    n_basis: PositiveInt = 16
+    r_max: PositiveFloat = 5.0
 
 
 BasisConfig = Union[GaussianBasisConfig, BesselBasisConfig]
@@ -84,6 +84,11 @@ class ShallowEnsembleConfig(BaseModel, extra="forbid"):
         If set to an integer, the jacobian of ensemble energies wrt. to positions will be computed
         in chunks of that size. This sacrifices some performance for the possibility to use relatively
         large ensemble sizes.
+
+    Hint
+    ----------
+    Loss type hase to be changed to a probabalistic loss like 'nll' or 'crps'
+
     """
 
     kind: Literal["shallow"] = "shallow"
@@ -101,12 +106,12 @@ class Correction(BaseModel, extra="forbid"):
 
 class ZBLRepulsion(Correction, extra="forbid"):
     name: Literal["zbl"]
-    r_max: NonNegativeFloat = 2.0
+    r_max: NonNegativeFloat = 1.5
 
 
 class ExponentialRepulsion(Correction, extra="forbid"):
     name: Literal["exponential"]
-    r_max: NonNegativeFloat = 2.0
+    r_max: NonNegativeFloat = 1.5
 
 
 EmpiricalCorrection = Union[ZBLRepulsion, ExponentialRepulsion]
@@ -120,31 +125,31 @@ class BaseModelConfig(BaseModel, extra="forbid"):
     ----------
     basis : BasisConfig, default = GaussianBasisConfig()
         Configuration for primitive basis funtions.
-    nn : List[PositiveInt], default = [512, 512]
+    nn : List[PositiveInt], default = [256, 256]
         Number of hidden layers and units in those layers.
-    w_init : Literal["normal", "lecun"], default = "normal"
+    w_init : Literal["normal", "lecun"], default = "lecun"
         Initialization scheme for the neural network weights.
-    b_init : Literal["normal", "zeros"], default = "normal"
+    b_init : Literal["normal", "zeros"], default = "zeros"
         Initialization scheme for the neural network biases.
-    use_ntk : bool, default = True
+    use_ntk : bool, default = False
         Whether or not to use NTK parametrization.
     ensemble : Optional[EnsembleConfig], default = None
         What kind of model ensemble to use (optional).
     use_zbl : bool, default = False
         Whether to include the ZBL correction.
     calc_stress : bool, default = False
         Whether to calculate stress during model evaluation.
-    descriptor_dtype : Literal["fp32", "fp64"], default = "fp64"
+    descriptor_dtype : Literal["fp32", "fp64"], default = "fp32"
         Data type for descriptor calculations.
     readout_dtype : Literal["fp32", "fp64"], default = "fp32"
         Data type for readout calculations.
-    scale_shift_dtype : Literal["fp32", "fp64"], default = "fp32"
+    scale_shift_dtype : Literal["fp32", "fp64"], default = "fp64"
         Data type for scale and shift parameters.
     """
 
     basis: BasisConfig = Field(BesselBasisConfig(name="bessel"), discriminator="name")
 
-    nn: List[PositiveInt] = [128, 128]
+    nn: List[PositiveInt] = [256, 256]
     w_init: Literal["normal", "lecun"] = "lecun"
     b_init: Literal["normal", "zeros"] = "zeros"
     use_ntk: bool = False

apax/config/train_config.py

@@ -118,7 +118,10 @@ class DataConfig(BaseModel, extra="forbid"):
         | dict of property name, shape (ragged or fixed) pairs. Currently unused.
     energy_regularisation :
         | Magnitude of the regularization in the per-element energy regression.
-
+    pos_unit : str, default = "Ang"
+        unit of length
+    energy_unit : str, default = "eV"
+        unit of energy
     """
 
     directory: str
@@ -210,16 +213,20 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     ----------
     name : str, default = "adam"
         Name of the optimizer. Can be any `optax` optimizer.
-    emb_lr : NonNegativeFloat, default = 0.02
+    emb_lr : NonNegativeFloat, default = 0.001
         Learning rate of the elemental embedding contraction coefficients.
-    nn_lr : NonNegativeFloat, default = 0.03
+    nn_lr : NonNegativeFloat, default = 0.001
         Learning rate of the neural network parameters.
-    scale_lr : NonNegativeFloat, default = 0.001
+    scale_lr : NonNegativeFloat, default = 0.0001
         Learning rate of the elemental output scaling factors.
-    shift_lr : NonNegativeFloat, default = 0.05
+    shift_lr : NonNegativeFloat, default = 0.003
         Learning rate of the elemental output shifts.
-    zbl_lr : NonNegativeFloat, default = 0.001
+    zbl_lr : NonNegativeFloat, default = 0.0001
         Learning rate of the ZBL correction parameters.
+    rep_scale_lr : NonNegativeFloat, default = 0.001
+        LR for the length scale of thes exponential repulsion potential.
+    rep_prefactor_lr : NonNegativeFloat, default = 0.0001
+        LR for the strength of the exponential repulsion potential.
     gradient_clipping: NonNegativeFloat, default = 1000.0
         Per element Gradient clipping value.
         Default is so high that it effectively disabled.
@@ -230,11 +237,11 @@ class OptimizerConfig(BaseModel, frozen=True, extra="forbid"):
     """
 
     name: str = "adam"
-    emb_lr: NonNegativeFloat = 0.02
-    nn_lr: NonNegativeFloat = 0.03
-    scale_lr: NonNegativeFloat = 0.001
-    shift_lr: NonNegativeFloat = 0.05
-    zbl_lr: NonNegativeFloat = 0.001
+    emb_lr: NonNegativeFloat = 0.001
+    nn_lr: NonNegativeFloat = 0.001
+    scale_lr: NonNegativeFloat = 0.0001
+    shift_lr: NonNegativeFloat = 0.003
+    zbl_lr: NonNegativeFloat = 0.0001
     rep_scale_lr: NonNegativeFloat = 0.001
     rep_prefactor_lr: NonNegativeFloat = 0.0001
 
@@ -362,7 +369,7 @@ class CheckpointConfig(BaseModel, extra="forbid"):
     reset_layers: List of layer names for which the parameters will be reinitialized.
     """
 
-    ckpt_interval: PositiveInt = 1
+    ckpt_interval: PositiveInt = 500
     base_model_checkpoint: Optional[str] = None
     reset_layers: List[str] = []
 

apax/md/simulate.py

@@ -17,7 +17,7 @@
 from apax.config import Config, MDConfig, parse_config
 from apax.config.md_config import Integrator
 from apax.md.ase_calc import make_ensemble, maybe_vmap
-from apax.md.constraints import Constraint, ConstraintBase, FixAtoms
+from apax.md.constraints import Constraint, ConstraintBase
 from apax.md.dynamics_checks import DynamicsCheckBase, DynamicsChecks
 from apax.md.io import H5TrajHandler, TrajHandler, truncate_trajectory_to_checkpoint
 from apax.md.md_checkpoint import load_md_state
@@ -255,8 +255,6 @@ def run_sim(
         dynamics_checks,
     )
 
-    constraints = [FixAtoms(indices=[6, 8])]
-
     apply_constraints = create_constraint_function(
         constraints,
         state,

apax/nodes/optimizer/__init__.py

@@ -0,0 +1,3 @@
+from apax.optimizer.get_optimizer import get_opt
+
+__all__ = ["get_opt"]