working md example and fully documented md driver code

examples/InPSNAPExample.py

@@ -1,3 +1,17 @@
+"""
+Example training to the SNAP database for Indium Phosphide.
+
+This script was designed for an external dataset available at
+https://github.com/FitSNAP/FitSNAP
+
+For info on the dataset, see the following publication:
+Explicit Multielement Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
+M. A. Cusentino, M. A. Wood, and A. P. Thompson
+The Journal of Physical Chemistry A 2020 124 (26), 5456-5464
+DOI: 10.1021/acs.jpca.0c02450
+
+"""
+
 import numpy as np
 import torch
 torch.set_default_dtype(torch.float32)

examples/ani1x_training.py

@@ -21,7 +21,7 @@
 import ase.units
 
 import sys
-sys.path.append("../../datasets/ani-al/readers/lib/")
+sys.path.append("../../datasets/ani-al/readers/lib/")  # location of pyanitools.py
 
 import pyanitools
 

examples/ani_aluminum_example.py

@@ -23,7 +23,7 @@
 
 import sys
 
-sys.path.append("../../datasets/ani-al/readers/lib/")
+sys.path.append("../../datasets/ani-al/readers/lib/")  # location of pyanitools.py
 import pyanitools  # Check if pyanitools is found early
 
 import torch

examples/ani_aluminum_example_multilayer.py

@@ -23,7 +23,7 @@
 
 import sys
 
-sys.path.append("../../datasets/ani-al/readers/lib/")
+sys.path.append("../../datasets/ani-al/readers/lib/")  # location of pyanitools.py
 import pyanitools  # Check if pyanitools is found early
 
 import torch

examples/ase_example.py

@@ -1,7 +1,9 @@
 """
 Script running an aluminum model with ASE.
-For training see `ani_aluminum_example.py`.
-This will generate the files for a model.
+
+Before running this script, you must run 
+`ani_aluminum_example.py` to train the corresponding
+model.
 
 Modified from ase MD example.
 
@@ -12,7 +14,6 @@
 # Imports
 import numpy as np
 import torch
-import hippynn
 import ase
 import time
 
@@ -45,15 +46,16 @@
     nrep = 10  # 1,000 atoms.
 
 # Build the atoms object
-atoms = ase.build.bulk("Al", crystalstructure="fcc", a=4.05)
+atoms = ase.build.bulk("Al", crystalstructure="fcc", a=4.05, orthorhombic=True)
 reps = nrep * np.eye(3, dtype=int)
 atoms = ase.build.make_supercell(atoms, reps, wrap=True)
 atoms.calc = calc
 
 print("Number of atoms:", len(atoms))
 
-atoms.rattle(0.1)
-MaxwellBoltzmannDistribution(atoms, temperature_K=500)
+rng = np.random.default_rng(seed=0)
+atoms.rattle(0.1, rng=rng)
+MaxwellBoltzmannDistribution(atoms, temperature_K=500, rng=rng)
 dyn = VelocityVerlet(atoms, 1 * units.fs)
 
 

examples/ase_example_multilayer.py

@@ -1,12 +1,15 @@
 """
 Script running an aluminum model with ASE.
-For training see `ani_aluminum_example.py`.
-This will generate the files for a model.
 
-Modified from ase MD example.
+This script is designed to match the 
+LAMMPS script located at 
+./lammps/in.mliap.unified.hippynn.Al
+
+Before running this script, you must run 
+`ani_aluminum_example_multilayer.py` to 
+train the corresponding model.
 
-If a GPU is available, this script
-will use it, and run a somewhat bigger system.
+Modified from ase MD example.
 """
 
 # Imports
@@ -29,7 +32,7 @@
     with active_directory("TEST_ALUMINUM_MODEL_MULTILAYER", create=False):
         bundle = load_checkpoint_from_cwd(map_location='cpu',restore_db=False)
 except FileNotFoundError:
-    raise FileNotFoundError("Model not found, run ani_aluminum_example.py first!")
+    raise FileNotFoundError("Model not found, run ani_aluminum_example_multilayer.py first!")
 
 model = bundle["training_modules"].model
 
@@ -38,15 +41,14 @@
 energy_node = model.node_from_name("energy")
 calc = HippynnCalculator(energy_node, en_unit=units.eV)
 calc.to(torch.float64)
+
 if torch.cuda.is_available():
-    nrep = 4
     calc.to(torch.device('cuda'))
-else:
-    nrep = 10
 
 # Build the atoms object
 atoms = FaceCenteredCubic(directions=np.eye(3, dtype=int),
                           size=(1,1,1), symbol='Al', pbc=(True,True,True))
+nrep = 4
 reps = nrep*np.eye(3, dtype=int)
 atoms = ase.build.make_supercell(atoms, reps, wrap=True)
 atoms.calc = calc

examples/close_contact_finding.py

@@ -15,7 +15,7 @@
 """
 import sys
 
-sys.path.append("../../datasets/ani-al/readers/lib/")
+sys.path.append("../../datasets/ani-al/readers/lib/")  # location of pyanitools.py
 import pyanitools  # Check if pyanitools is found early
 
 ### Loading the database

examples/feature_extraction.py

@@ -64,7 +64,7 @@
 
 import sys
 
-sys.path.append("../../datasets/ani-al/readers/lib/")
+sys.path.append("../../datasets/ani-al/readers/lib/")  # location of pyanitools.py
 import pyanitools  # Check if pyanitools is found early
 from hippynn.databases.h5_pyanitools import PyAniDirectoryDB
 

examples/molecular_dynamics_standard.py

@@ -33,7 +33,7 @@
 )
 
 if torch.cuda.is_available():
-    nrep = 10
+    nrep = 30
     device = "cuda"
 else:
     nrep = 10
@@ -52,23 +52,23 @@
 energy_node = model.node_from_name("energy")
 force_node = physics.GradientNode("force", (energy_node, positions_node), sign=-1)
 
-# # Replace pair-finder with more efficient one (the HippynnCalculator also does this)
-# old_pairs_node = model.node_from_name("PairIndexer")
-# species_node = model.node_from_name("species")
-# cell_node = model.node_from_name("cell")
-# model.print_structure()
-# # PositionsNode, Encoder, PaddingIndexer, CellNode
-# new_pairs_node = KDTreePairsMemory("PairIndexer", parents=(positions_node, species_node, cell_node), skin=2, dist_hard_max=7.5)
-# hippynn_node = model.node_from_name("HIPNN")
-# print(hippynn_node.parents)
-# replace_node(old_pairs_node, new_pairs_node)
+# Replace pair-finder with more efficient one (the HippynnCalculator also does this)
+old_pairs_node = model.node_from_name("PairIndexer")
+species_node = model.node_from_name("species")
+cell_node = model.node_from_name("cell")
+model.print_structure()
+# PositionsNode, Encoder, PaddingIndexer, CellNode
+new_pairs_node = KDTreePairsMemory("PairIndexer", parents=(positions_node, species_node, cell_node), skin=2, dist_hard_max=7.5)
+hippynn_node = model.node_from_name("HIPNN")
+print(hippynn_node.parents)
+replace_node(old_pairs_node, new_pairs_node)
 
 model = Predictor(inputs=model.input_nodes, outputs=[force_node])
 model.to(device)
 model.to(torch.float64)
 
 # Use ASE to generate initial positions and velocities
-atoms = ase.build.bulk("Al", crystalstructure="fcc", a=4.05)
+atoms = ase.build.bulk("Al", crystalstructure="fcc", a=4.05, orthorhombic=True)
 reps = nrep * np.eye(3, dtype=int)
 atoms = ase.build.make_supercell(atoms, reps, wrap=True)
 

examples/singlet_triplet_model.py

@@ -1,3 +1,5 @@
+# NOTE: This script needs revision before it will run
+
 import torch
 
 # Setup pytorch things

hippynn/layers/pairs/dispatch.py

@@ -138,7 +138,7 @@ def neighbor_list_torch(cutoff: float, coords, cell):
 def neighbor_list_kdtree(cutoff, coords, cell):
     '''
     Use KD Tree implementation from scipy.spatial to find pairs under periodic boundary conditions 
-    with an orthonormal cell.
+    with an orthorhombic cell.
     '''
 
     # Verify that cell is orthorhombic