lammps/examples/mliap

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. It uses a ReLU activation
function with just 1 hidden layer.
  
`lmp -in in.mliap.pytorch.relu1hidden -echo both`