Building a Model¶

To modify a simulation, you create a Keras tf.keras.Model that will be executed at each step (or some multiple of steps) during the simulation. See the Using a Model in a Simulation to see how to train your model instead, though these instructions still apply.

To begin subclass a SimModel class:

import hoomd.htf as htf
class MyModel(htf.SimModel):
def compute(self, nlist, positions, box):
...
return forces, other, important, quantities

model = MyModel(NN, output_forces=True)


where NN is the maximum number of nearest neighbors to consider (can be 0). This is an upper-bound, so choose a large number. If you are unsure, you can guess and add check_nlist = True to your constructor. This will cause the program to halt if you choose too low. output_forces indicates if the model will output forces to use in the simulation. In the SimModel.compute(nlist, positions, box) function you will have three tensors that can be used:

• nlist is an N x NN x 4 tensor containing the nearest neighbors. An entry of all zeros indicates that less than NN nearest neighbors where present for a particular particle. The 4 right-most dimensions are x,y,z and w, which is the particle type. Particle type is an integer starting at 0. Note that the x,y,z values are a vector originating at the particle and ending at its neighbor.
• positions is an N x 4 tensor of particle positions (x,y,z) and type.
• box is a 3x3 tensor containing the low box coordinate (row 0), high box coordinate (row 1), and then tilt factors (row 2).

Your function can use fewer tensors, like compute(self, nlist) if desired.

Keras Model¶

Your models are Keras :obj:tf.keras.Models so that all the usual process of building layers, saving, and computing metrics apply. For example, here is a two hidden layer neural network force-field that uses the 8 nearest neighbors to compute forces.

class NlistNN(htf.SimModel):
def setup(self, dim, top_neighs):
self.dense1 = tf.keras.layers.Layer(dim)
self.dense2 = tf.keras.layers.Layer(dim)
self.last = tf.keras.layers.Layer(1)
self.top_neighs = top_neighs

def compute(self, nlist):
rinv = htf.nlist_rinv(nlist)
# closest neighbors have largest value in 1/r, take top
top_n = tf.sort(rinv, axis=1, direction='DESCENDING')[
:, :self.top_neighs]
# run through NN
x = self.dense1(top_n)
x = self.dense2(x)
energy = self.last(x)
forces = htf.compute_nlist_forces(nlist, energy)
return forces
model = NlistNN(64, dim=16, top_neighs=8)


The 64 is the nlist size, dim is the hidden layer dimension, and top_neighs is how many neighbors to consider. Refer to the Keras documentation to learn more about models.

Molecule Batching¶

It may be simpler to have positions or neighbor lists or forces arranged by molecule. For example, you may want to look at only a particular bond or subset of atoms in a molecule. To do this, you can instead subclass MolSimModel:

import hoomd.htf as htf
class MyModel(htf.SimModel):
def mol_compute(self, nlist, positions, mol_nlist, mol_pos, box):
...
return forces, other, important, quantities

model = MyModel(MN, NN, mol_indices)


whose argument MN is the maximum number of atoms in a molecule and mol_indices describes the molecules in your system as a list of atom indices. This can be created directly from a hoomd system via find_molecules(). The mol_indices are a, possibly ragged, 2D python list where each element in the list is a list of atom indices for a molecule. For example, [[0,1], [1]] means that there are two molecules with the first containing atoms 0 and 1 and the second containing atom 1. Note that the molecules can be different size and atoms can exist in multiple molecules.

MolSimModel.mol_compute(self, nlist, positions, mol_nlist, mol_pos) has the following additional arguments: mol_positions and mol_nlist. These new attributes are dimension M x MN x ... where M is the number of molecules and MN is the atom index within the molecule. If your molecule has fewer than MN atoms, extra entries will be zeros. You can define a molecule to be whatever you want, and atoms need not be only in one molecule. Here’s an example to compute a water angle, where we assume that the oxygens are the middle atom:

import hoomd.htf as htf

class MyModel(htf.SimModel):
def mol_compute(self, nlist, positions, mol_nlist, mol_pos):
# want slice for all molecules (:)
# want h1 (0), o (1), h2(2)
# positions are x,y,z,w. We only want x,y z (:3)
v1 = mol_pos[:, 2, :3] - mol_pos[:, 1, :3]
v2 = mol_pos[:, 0, :3] - mol_pos[:, 1, :3]
# compute per-molecule dot product and divide by per molecule norm
c = tf.einsum('ij,ij->i', v1, v2) / tf.norm(v1, axis=1) / tf.norm(v2 axis=1)
angles = tf.math.acos(c)
return angles

# ...set-up hoomd...
mol_indices = htf.find_molecules(hoomd_system)
model = MyModel(3, 128, mol_indices, output_forces=False)


Computing Forces¶

If your graph is outputting forces, they must be the first return value from your compute method. It is easiest to compute forces using the automatic differentiation of a potential energy. Call compute_nlist_forces() with the argument nlist and energy. energy can be either a scalar or a tensor which depends on nlist. A tensor of forces will be returned as $$\sum_i(\frac{-\partial E} {\partial n_i})$$, where the sum is over the neighbor list. For example, to compute a $$1 / r$$ potential:

import hoomd.htf as htf
class MyModel(htf.SimModel):
def compute(self, nlist, positions):
#remove w since we don't care about types
r = tf.norm(nlist[:, :, :3], axis=2)
pairwise_energy = 0.5 * tf.math.divide_no_nan(1, r)
# sum over neighbors
energy = tf.reduce_sum(pairwise_energy, axis = 1)
forces = htf.compute_nlist_forces(nlist, energy)
return forces


Notice that in the above example that we have used the tf.math.divide_no_nan method, which allows us to safely treat a $$1 / 0$$, which can arise because nlist contains 0s for when fewer than NN nearest neighbors are found.

There is also a method compute_positions_forces(positions, energy) which can be used to compute position dependent forces.

Note: because nlist is a full neighbor list, you should divide by 2 if your energy is a sum of pairwise energies.

Neighbor lists¶

The nlist is an N x NN x 4 neighbor list tensor. You can compute a masked versions of this with masked_nlist(nlist, type_tensor, type_i, type_j) where type_i and type_j are optional integers that specify the type of the origin (type_i) or neighbor (type_j). type_tensor is positions[:,3] or your own types can be chosen. You can also use nlist_rinv(nlist) which gives a pre-computed 1 / r (dimension N x NN).

Virial¶

A virial term can be added by doing both of the following extra steps:

1. Compute virial with your forces compute_nlist_forces(nlist, energy,virial=True) by adding the virial=True arg.
2. Add the modify_virial=True argument to your model constructor

Mapped quantities¶

If mapped quantities are desired for coarse-graining while running a simulation, you can call tfcompute.enable_mapped_nlist(system, mapping_fxn) to utilize hoomd to compute fast neighbor lists. The model code can then use SimModel.mapped_nlist() and SimModel.mapped_positions() to access mapped nlist and positions. An example:

import hoomd.htf as htf

def mapping_fxn(AA):
return M @ AA

class MyModel(htf.SimModel):
def compute(self, nlist, positions, forces):
aa_nlist, mapped_nlist = self.mapped_nlist(nlist)
aa_pos, mapped_pos = self.mapped_positions(positions)

...

tfcompute.enable_mapped_nlist(system, mapping_fxn)


Call tfcompute.enable_mapped_nlist() prior to running the simulation.

To save a model:

Because these models do not use standard Keras objects, to reload a model you must first use your python code to build the model and then load weights into from a file like so:

tmp_loaded_model = tf.keras.load_model('/path/to/model')
model = MyModel(16, output_forces=True)


Complete Examples¶

The file htf/test-py/build_examples.py contains example models

Lennard-Jones with 1 Particle Type¶

class LJModel(htf.SimModel):
def compute(self, nlist):
# get r
rinv = htf.nlist_rinv(nlist)
inv_r6 = rinv**6
# pairwise energy. Double count -> divide by 2
p_energy = 4.0 / 2.0 * (inv_r6 * inv_r6 - inv_r6)
# sum over pairwise energy
energy = tf.reduce_sum(input_tensor=p_energy, axis=1)
forces = htf.compute_nlist_forces(nlist, energy)
return forces