Merge pull request #154 from dingye18/devel

Add aux support in ml.py for MLP. Fix jit error when saving ml potential with save_dmff2tf.py

backend/openmm_dmff_plugin/openmmapi/include/DMFFForce.h

@@ -77,7 +77,17 @@ class OPENMM_EXPORT_DMFF DMFFForce : public OpenMM::Force {
      * @param hasAux  : true if model was saved with auxilary input.
      */
     void setHasAux(const bool hasAux);
-
+    /**
+     * @brief Set the Cutoff for neighbor list fetching.
+     * 
+     * @param cutoff 
+     */
+    void setCutoff(const double cutoff);
+    /**
+     * @brief get the DMFF graph file.
+     * 
+     * @return const std::string& 
+     */
     const std::string& getDMFFGraphFile() const;
     /**
      * @brief Get the Coord Unit Coefficient.

backend/openmm_dmff_plugin/openmmapi/src/DMFFForce.cpp

@@ -58,6 +58,10 @@ void DMFFForce::setHasAux(const bool hasAux){
     this->has_aux = hasAux;
 }
 
+void DMFFForce::setCutoff(const double cutoff){
+    this->cutoff = cutoff;
+}
+
 double DMFFForce::getCoordUnitCoefficient() const {return coordCoeff;}
 double DMFFForce::getForceUnitCoefficient() const {return forceCoeff;}
 double DMFFForce::getEnergyUnitCoefficient() const {return energyCoeff;}

backend/openmm_dmff_plugin/python/OpenMMDMFFPlugin.i

@@ -48,7 +48,7 @@ public:
 
     void setUnitTransformCoefficients(const double coordCoefficient, const double forceCoefficient, const double energyCoefficient);
     void setHasAux(const bool hasAux);
-
+    void setCutoff(const double cutoff);
     /*
      * Add methods for casting a Force to a DMFFForce.
     */

backend/openmm_dmff_plugin/python/OpenMMDMFFPlugin/tools.py

@@ -78,6 +78,16 @@ def setHasAux(self, has_aux = False):
             has_aux (bool, optional): Defaults to False.
         """
         self.dmff_force.setHasAux(has_aux)
+        return
+
+    def setCutoff(self, cutoff = 1.2):
+        """Set the cutoff for the DMFF model.
+
+        Args:
+            cutoff (float, optional): Defaults to 1.2.
+        """
+        self.dmff_force.setCutoff(cutoff)
+        return
 
     def createSystem(self, topology):
         """Create the OpenMM System object for the DMFF model.

dmff/eann/eann.py

@@ -247,7 +247,7 @@ def get_features(self, radial, dr, pairs, buffer_scales, orb_coeff):
         f_cut = cutoff_cosine(dr_norm, self.rc)
         neigh_list = jnp.concatenate((pairs,pairs[:,[1,0]]),axis=0)
         buffer_scales_ = jnp.concatenate((buffer_scales,buffer_scales),axis=0)
-        totneighbour = len(neigh_list)
+        totneighbour = neigh_list.shape[0]
         prefacs = f_cut.reshape(1, -1)
         angular = prefacs
         for ipsin in range(1,self.nipsin+1):
@@ -310,7 +310,8 @@ def get_energy(positions, box, pairs, params):
             self.rs = params['density.rs']
             self.inta = params['density.inta']
 
-            radial_i, radial_j = get_gto(jnp.arange(len(dr_norm)), dr_norm, pairs, self.rc, self.rs, self.inta, self.elem_indices)
+            length_dr_norm = dr_norm.shape[0]
+            radial_i, radial_j = get_gto(jnp.arange(length_dr_norm), dr_norm, pairs, self.rc, self.rs, self.inta, self.elem_indices)
             radial = jnp.concatenate((radial_i,radial_j), axis=0)
             orb_coeff = params['density.params'][self.elem_indices,:] # (48,16)
 

dmff/generators/ml.py

@@ -116,12 +116,19 @@ def createPotential(self, topdata: DMFFTopology, nonbondedMethod, nonbondedCutof
         n_elem, elem_indices = get_elem_indices(self.ommtopology)
         self.model = EANNForce(n_elem, elem_indices, n_gto=self.ngto, nipsin=self.nipsin, rc=self.rc)
         n_layers = self.model.n_layers
-        def potential_fn(positions, box, pairs, params):
+
+        has_aux = False
+        if "has_aux" in kwargs and kwargs["has_aux"]:
+            has_aux = True
+
+        def potential_fn(positions, box, pairs, params, aux=None):
             # convert unit to angstrom
             positions = positions * 10
             box = box * 10
-
-            return self.model.get_energy(positions, box, pairs, params[self.name])
+            if has_aux:
+                return self.model.get_energy(positions, box, pairs, params[self.name]), aux
+            else:
+                return self.model.get_energy(positions, box, pairs, params[self.name])
 
         self._jaxPotential = potential_fn
         return potential_fn