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Aidan Thompson
2020-12-23 10:17:55 -07:00
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examples/mliap/README
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This directory contains multipler examples of
This directory contains multiple examples of
machine-learning potentials defined using the
MLIAP package in LAMMPS. The input files
are descirbed below.
are described below.
in.mliap.snap.Ta06A
-------------------
@ -21,58 +21,62 @@ Run EME-SNAP, equivalent to examples/snap/in.snap.InP.JCPA2020
in.mliap.snap.compute
---------------------
Generate gradients w.r.t. coefficients for linear SNAP,
equivalent to in.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 gradients w.r.t. coefficients for quadratic SNAP,
equivalent to in.snap.compute.quadratic
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.
It can be run in two different ways:
This example can be run in two different ways:
1: Running a LAMMPS executable: in.mliap.pytorch.Ta06A
First run convert_mliap_Ta06A.py, which will convert the Ta06A potential
into a pytorch model. It will be saved as "Ta06A.mliap.pytorch.model.pkl".
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".
It will also copy "../../src/MLIAP/mliappy_pytorch.py"
file into the current working directory. mliappy_pytorch.py contains
class definitions suitable for wrapping an arbitrary PyTorch
energy model. It must be available to python when
creating or unpickling a PyTorch energy model.
From that point you can run the example as follows
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 first example
(using LAMMPS executable) works.
Also, not all python installations support this mode of operation.
It requires that the Python interpreter be initialized.
To check this for your Python library,
try running the Py_IsInitialized() method.
If the return value is True, you should be able to run the example,
as follows:
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
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