113 lines
3.6 KiB
Plaintext
113 lines
3.6 KiB
Plaintext
This directory contains multiple examples of
|
|
machine-learning potentials defined using the
|
|
MLIAP package in LAMMPS. The input files
|
|
are described below.
|
|
|
|
in.mliap.snap.Ta06A
|
|
-------------------
|
|
Run linear SNAP, equivalent to examples/snap/in.snap.Ta06A
|
|
|
|
in.mliap.snap.WBe.PRB2019
|
|
-------------------------
|
|
Run linear SNAP, equivalent to examples/snap/in.snap.WBe.PRB2019
|
|
|
|
in.mliap.snap.quadratic
|
|
-----------------------
|
|
Run quadratic SNAP
|
|
|
|
in.mliap.snap.chem
|
|
------------------
|
|
Run EME-SNAP, equivalent to examples/snap/in.snap.InP.JCPA2020
|
|
|
|
in.mliap.snap.compute
|
|
---------------------
|
|
Generate the A matrix, the gradients (w.r.t. coefficients)
|
|
of total potential energy, forces, and stress tensor for
|
|
linear SNAP, equivalent to in.snap.compute
|
|
|
|
in.mliap.quadratic.compute
|
|
--------------------------
|
|
Generate the A matrix, the gradients (w.r.t. coefficients)
|
|
of total potential energy, forces, and stress tensor for
|
|
for quadratic SNAP, equivalent to in.snap.compute.quadratic
|
|
|
|
in.mliap.pytorch.Ta06A
|
|
-----------------------
|
|
This reproduces the output of in.mliap.snap.Ta06A above,
|
|
but using the Python coupling to PyTorch.
|
|
|
|
This example can be run in two different ways:
|
|
|
|
1: Running a LAMMPS executable: in.mliap.pytorch.Ta06A
|
|
|
|
First run ``python convert_mliap_Ta06A.py``. It creates
|
|
a PyTorch energy model that replicates the
|
|
SNAP Ta06A potential and saves it in the file
|
|
"Ta06A.mliap.pytorch.model.pt".
|
|
|
|
You can then run the example as follows
|
|
|
|
`lmp -in in.mliap.pytorch.Ta06A -echo both`
|
|
|
|
The resultant log.lammps output should be identical to that generated
|
|
by in.mliap.snap.Ta06A.
|
|
|
|
If this fails, see the instructions for building the MLIAP package
|
|
with Python support enabled. Also, confirm that the
|
|
LAMMPS Python embedded Python interpreter is
|
|
working by running ../examples/in.python.
|
|
|
|
2: Running a Python script: mliap_pytorch_Ta06A.py
|
|
|
|
Before testing this, ensure that the previous method
|
|
(running a LAMMPS executable) works.
|
|
|
|
You can run the example in serial:
|
|
|
|
`python mliap_pytorch_Ta06A.py`
|
|
|
|
or in parallel:
|
|
|
|
`mpirun -np 4 python mliap_pytorch_Ta06A.py`
|
|
|
|
The resultant log.lammps output should be identical to that generated
|
|
by in.mliap.snap.Ta06A and in.mliap.pytorch.Ta06A.
|
|
|
|
Not all Python installations support this mode of operation.
|
|
It requires that the Python interpreter be initialized. If not,
|
|
the script will exit with an error message.
|
|
|
|
in.mliap.pytorch.relu1hidden
|
|
----------------------------
|
|
This example demonstrates a simple neural network potential
|
|
using PyTorch and SNAP descriptors.
|
|
|
|
`lmp -in in.mliap.pytorch.relu1hidden -echo both`
|
|
|
|
It was trained on just the energy component (no forces) of
|
|
the data used in the original SNAP Ta06A potential for
|
|
tantalum (Thompson, Swiler, Trott, Foiles, Tucker,
|
|
J Comp Phys, 285, 316 (2015).). Because of the very small amount
|
|
of energy training data, it uses just 1 hidden layer with
|
|
a ReLU activation function. It is not expected to be
|
|
very accurate for forces.
|
|
|
|
NOTE: Unlike the previous example, this example uses
|
|
a pre-built PyTorch file `Ta06A.mliap.pytorch.model.pt`.
|
|
It is read using `torch.load`,
|
|
which implicitly uses the Python `pickle` module.
|
|
This is known to be insecure. It is possible to construct malicious
|
|
pickle data that will execute arbitrary code during unpickling. Never
|
|
load data that could have come from an untrusted source, or that
|
|
could have been tampered with. Only load data you trust.
|
|
|
|
in.mliap.nn.Ta06A
|
|
-------------------
|
|
Run linear SNAP using the "nn" model style, equivalent to examples/snap/in.snap.Ta06A
|
|
|
|
in.mliap.nn.cu
|
|
-------------------------
|
|
Run a neural network potential for Cu, a combination of SNAP descriptors and the "nn" model style
|
|
|
|
|