Copy MLIAPUnified LJ example

examples/mliap/jax/README.md

@@ -35,4 +35,8 @@ First define JAX model in `deploy_script.py`, which will wrap model with `write_
 
     python deploy_script.py
 
-Then load model in LAMMPS and run:
+This creates `.pkl` file to be loaded by LAMMPS ML-IAP Unified.
+
+Run LAMMPS with the model:
+
+    mpirun -np P lmp -in in.run

examples/mliap/jax/deploy_script.py

@@ -1,32 +1,11 @@
-import numpy as np
+import lammps
-import pickle
+import lammps.mliap
-from pathlib import Path
-from write_unified import MLIAPInterface
 
-class MyModel():
+#from lammps.mliap.mliap_unified_lj import MLIAPUnifiedLJ
-  def __init__(self,blah):
+from mliap_unified_jax import MLIAPUnifiedJAX
-      """
-      coeffs = np.genfromtxt(file,skip_header=6)
-      self.bias = coeffs[0]
-      self.weights = coeffs[1:]
-      """
-      self.blah = blah
-      self.n_params = 3 #len(coeffs)
-      self.n_descriptors = 1 #len(self.weights)
-      self.n_elements = 1
-
-  def __call__(self,rij):
-      print(rij)
-      #energy[:] = bispectrum @ self.weights + self.bias
-      #beta[:] = self.weights
-      return 5
-
-model = MyModel(1)
-
-#unified = MLIAPInterface(model, ["Ta"], model_device="cpu")
 
 def create_pickle():
-    unified = MLIAPInterface(model, ["Ta"])
+    unified = MLIAPUnifiedJAX(["Ar"])
-    unified.pickle('mliap_jax.pkl')
+    unified.pickle('mliap_unified_jax_Ar.pkl')
 
 create_pickle()

examples/mliap/jax/in.run

@@ -1,48 +1,35 @@
-# Initialize simulation
+# 3d Lennard-Jones melt
 
-variable nsteps index 10000
+units           lj
-units           metal
+atom_style      atomic
 
-# generate the box and atom positions using a BCC lattice
+lattice         fcc 0.8442
-
+region          box block 0 10 0 10 0 10
-#boundary        p p p
-#read_data DATA
-
-variable nrep equal 2 #10
-variable a equal 3.316
-variable nx equal ${nrep}
-variable ny equal ${nrep}
-variable nz equal ${nrep}
-lattice         bcc $a
-region          box block 0 ${nx} 0 ${ny} 0 ${nz}
 create_box      1 box
 create_atoms    1 box
-mass 1 180.88
+mass            1 1.0
 
-pair_style mliap unified mliap_jax.pkl 0
+velocity        all create 3.0 87287 loop geom
-pair_coeff * * Ta
 
-compute  eatom all pe/atom
+pair_style      mliap unified mliap_unified_jax_Ar.pkl 0
-compute  energy all reduce sum c_eatom
+pair_coeff      * * Ar
 
-compute  satom all stress/atom NULL
+neighbor        0.3 bin
-compute  str all reduce sum c_satom[1] c_satom[2] c_satom[3]
+neigh_modify    every 20 delay 0 check no
-variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
 
-thermo_style    custom step temp epair c_energy etotal press v_press
+fix             1 all nve
-thermo          10
-thermo_modify norm yes
 
-# Set up NVE run
+#dump           id all atom 50 dump.melt
 
-timestep 0.5e-3
+#dump           2 all image 25 image.*.jpg type type &
-neighbor 1.0 bin
+#               axes yes 0.8 0.02 view 60 -30
-# is this neigh modify every 1 slow?
+#dump_modify    2 pad 3
-neigh_modify once no every 1 delay 0 check yes
 
-# Run MD
+#dump           3 all movie 1 movie.mpg type type &
+#               axes yes 0.8 0.02 view 60 -30
+#dump_modify    3 pad 3
 
-velocity all create 3200.0 4928459 loop geom
+#dump 4 all custom 1 forces.xyz fx fy fz
-dump 1 all xyz 10 dump.xyz 
+
-fix 1 all nve
+thermo          50
-run             ${nsteps}
+run             250

examples/mliap/jax/mliap_unified_jax.py

@@ -0,0 +1,41 @@
+from lammps.mliap.mliap_unified_abc import MLIAPUnified
+import numpy as np
+
+
+class MLIAPUnifiedJAX(MLIAPUnified):
+    """Test implementation for MLIAPUnified."""
+
+    def __init__(self, element_types, epsilon=1.0, sigma=1.0, rcutfac=1.25):
+        # ARGS: interface, element_types, ndescriptors, nparams, rcutfac
+        super().__init__(None, element_types, 1, 3, rcutfac)
+        # Mimicking the LJ pair-style:
+        # pair_style lj/cut 2.5
+        # pair_coeff * * 1 1
+        self.epsilon = epsilon
+        self.sigma = sigma
+
+    def compute_gradients(self, data):
+        """Test compute_gradients."""
+
+    def compute_descriptors(self, data):
+        """Test compute_descriptors."""
+
+    def compute_forces(self, data):
+        """Test compute_forces."""
+        eij, fij = self.compute_pair_ef(data)
+        data.update_pair_energy(eij)
+        data.update_pair_forces(fij)
+
+    def compute_pair_ef(self, data):
+        rij = data.rij
+
+        r2inv = 1.0 / np.sum(rij ** 2, axis=1)
+        r6inv = r2inv * r2inv * r2inv
+
+        lj1 = 4.0 * self.epsilon * self.sigma**12
+        lj2 = 4.0 * self.epsilon * self.sigma**6
+
+        eij = r6inv * (lj1 * r6inv - lj2)
+        fij = r6inv * (3.0 * lj2 - 6.0 * lj2 * r6inv) * r2inv
+        fij = fij[:, np.newaxis] * rij
+        return eij, fij

examples/mliap/jax/write_unified.py

@@ -6,6 +6,7 @@ import pickle
 import numpy as np
 
 from lammps.mliap.mliap_unified_abc import MLIAPUnified
+#from deploy_script import MyModel
 
 class MLIAPInterface(MLIAPUnified):
     """