rm expcell batch

src/fairchem/core/common/relaxation/optimizers/optimizable.py

@@ -556,76 +556,3 @@ def get_forces(
 
     def __len__(self):
         return len(self.batch.pos) + 3 * len(self.batch)
-
-
-class OptimizableExpCellBatch(OptimizableBatch):
-    """Modify the supercell and the atom positions in relaxations.
-
-    Based on ase FrechetCellFilter to work on data batches and that in JuLIP.jl:
-        https://github.com/JuliaMolSim/JuLIP.jl/blob/master/src/expcell.jl
-    """
-
-    def __init__(
-        self,
-        batch: Batch,
-        trainer: BaseTrainer,
-        transform: torch.nn.Module | None = None,
-        numpy: bool = False,
-        mask_converged: bool = True,
-        cumulative_mask: bool = True,
-        mask: Sequence[bool] | None = None,
-        cell_factor: float | None = None,
-        hydrostatic_strain: bool = False,
-        constant_volume: bool = False,
-        scalar_pressure: float = 0.0,
-    ):
-        """Create a filter that returns the forces and unit cell stresses together, for simultaneous optimization.
-
-        For full details see:
-            E. B. Tadmor, G. S. Smith, N. Bernstein, and E. Kaxiras,
-            Phys. Rev. B 59, 235 (1999)
-
-        Args:
-            batch: A batch of atoms graph data
-            model: An instance of a BaseTrainer derived class
-            transform: graph transform
-            numpy: whether to cast results to numpy arrays
-            mask_converged: if true will mask systems in batch that are already converged
-            cumulative_mask: if true, once system is masked then it remains masked even if new predictions give forces
-                above threshold, ie. once masked always masked. Note if this is used make sure to check convergence with
-                the same fmax always
-            mask: a boolean mask specifying which strain components are allowed to relax
-            cell_factor:
-                Factor by which deformation gradient is multiplied to put
-                it on the same scale as the positions when assembling
-                the combined position/cell vector. The stress contribution to
-                the forces is scaled down by the same factor. This can be thought
-                of as a very simple preconditioners. Default is number of atoms
-                which gives approximately the correct scaling.
-            hydrostatic_strain:
-                Constrain the cell by only allowing hydrostatic deformation.
-                The virial tensor is replaced by np.diag([np.trace(virial)]*3).
-            constant_volume:
-                Project out the diagonal elements of the virial tensor to allow
-                relaxations at constant volume, e.g. for mapping out an
-                energy-volume curve. Note: this only approximately conserves
-                the volume and breaks energy/force consistency so can only be
-                used with optimizers that do require a line minimisation
-                (e.g. FIRE).
-            scalar_pressure:
-                Applied pressure to use for enthalpy pV term. As above, this
-                breaks energy/force consistency.
-        """
-        super().__init__(
-            batch=batch,
-            trainer=trainer,
-            transform=transform,
-            numpy=numpy,
-            mask_converged=mask_converged,
-            cumulative_mask=cumulative_mask,
-            mask=mask,
-            cell_factor=cell_factor,
-            hydrostatic_strain=hydrostatic_strain,
-            constant_volume=constant_volume,
-            scalar_pressure=scalar_pressure,
-        )