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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.

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# DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
# Definition of SNAP potential Ta_Cand06A
# Assumes 1 LAMMPS atom type
variable zblcutinner equal 4
variable zblcutouter equal 4.8
variable zblz equal 73
# Specify hybrid with SNAP, ZBL
pair_style hybrid/overlay &
zbl ${zblcutinner} ${zblcutouter} &
mliap model mliappy Ta06A.mliap.pytorch.model.pt &
descriptor sna Ta06A.mliap.descriptor
pair_coeff 1 1 zbl ${zblz} ${zblz}
pair_coeff * * mliap Ta

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import sys
import numpy as np
import torch
# torch.nn.modules useful for defining a MLIAPPY model.
from lammps.mliap.pytorch import TorchWrapper, IgnoreElems
# Read coefficients
coeffs = np.genfromtxt("Ta06A.mliap.model",skip_header=6)
# Write coefficients to a pytorch linear model
bias = coeffs[0]
weights = coeffs[1:]
lin = torch.nn.Linear(weights.shape[0],1)
lin.to(torch.float64)
with torch.autograd.no_grad():
lin.weight.set_(torch.from_numpy(weights).unsqueeze(0))
lin.bias.set_(torch.as_tensor(bias,dtype=torch.float64).unsqueeze(0))
# Wrap the pytorch model for usage with mliappy coupling.
model = IgnoreElems(lin) # The linear module does not use the types.
n_descriptors = lin.weight.shape[1]
n_elements = 1
linked_model = TorchWrapper(model,n_descriptors=n_descriptors,n_elements=n_elements)
torch.save(linked_model,"Ta06A.mliap.pytorch.model.pt")

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# Demonstrate MLIAP/PyTorch interface to linear SNAP potential
# Initialize simulation
variable nsteps index 100
variable nrep equal 4
variable a equal 3.316
units metal
# generate the box and atom positions using a BCC lattice
variable nx equal ${nrep}
variable ny equal ${nrep}
variable nz equal ${nrep}
boundary p p p
lattice bcc $a
region box block 0 ${nx} 0 ${ny} 0 ${nz}
create_box 1 box
create_atoms 1 box
mass 1 180.88
# choose potential
include Ta06A.mliap.pytorch
# Setup output
compute eatom all pe/atom
compute energy all reduce sum c_eatom
compute satom all stress/atom NULL
compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
thermo_style custom step temp epair c_energy etotal press v_press
thermo 10
thermo_modify norm yes
# Set up NVE run
timestep 0.5e-3
neighbor 1.0 bin
neigh_modify once no every 1 delay 0 check yes
# Run MD
velocity all create 300.0 4928459 loop geom
fix 1 all nve
run ${nsteps}

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# Demonstrate MLIAP interface to linear SNAP potential
# Initialize simulation
variable nsteps index 100
variable nrep equal 4
variable a equal 3.316
units metal
# generate the box and atom positions using a BCC lattice
variable nx equal ${nrep}
variable ny equal ${nrep}
variable nz equal ${nrep}
boundary p p p
lattice bcc $a
region box block 0 ${nx} 0 ${ny} 0 ${nz}
create_box 1 box
create_atoms 1 box
mass 1 180.88
# choose potential
include relu1hidden.mliap.pytorch
# Setup output
compute eatom all pe/atom
compute energy all reduce sum c_eatom
compute satom all stress/atom NULL
compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
thermo_style custom step temp epair c_energy etotal press v_press
thermo 10
thermo_modify norm yes
# Set up NVE run
timestep 0.5e-3
neighbor 1.0 bin
neigh_modify once no every 1 delay 0 check yes
# Run MD
velocity all create 300.0 4928459 loop geom
fix 1 all nve
run ${nsteps}

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# Demonstrate MLIAP interface to kinear SNAP potential
# Demonstrate MLIAP interface to linear SNAP potential
# Initialize simulation

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LAMMPS (30 Nov 2020)
using 48 OpenMP thread(s) per MPI task
# Demonstrate MLIAP/PyTorch interface to linear SNAP potential
# Initialize simulation
variable nsteps index 100
variable nrep equal 4
variable a equal 3.316
units metal
# generate the box and atom positions using a BCC lattice
variable nx equal ${nrep}
variable nx equal 4
variable ny equal ${nrep}
variable ny equal 4
variable nz equal ${nrep}
variable nz equal 4
boundary p p p
lattice bcc $a
lattice bcc 3.316
Lattice spacing in x,y,z = 3.3160000 3.3160000 3.3160000
region box block 0 ${nx} 0 ${ny} 0 ${nz}
region box block 0 4 0 ${ny} 0 ${nz}
region box block 0 4 0 4 0 ${nz}
region box block 0 4 0 4 0 4
create_box 1 box
Created orthogonal box = (0.0000000 0.0000000 0.0000000) to (13.264000 13.264000 13.264000)
1 by 1 by 1 MPI processor grid
create_atoms 1 box
Created 128 atoms
create_atoms CPU = 0.002 seconds
mass 1 180.88
# choose potential
include Ta06A.mliap.pytorch
# DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
# Definition of SNAP potential Ta_Cand06A
# Assumes 1 LAMMPS atom type
variable zblcutinner equal 4
variable zblcutouter equal 4.8
variable zblz equal 73
# Specify hybrid with SNAP, ZBL
pair_style hybrid/overlay zbl ${zblcutinner} ${zblcutouter} mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
pair_style hybrid/overlay zbl 4 ${zblcutouter} mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
pair_style hybrid/overlay zbl 4 4.8 mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
Loading python model complete.
Reading potential file Ta06A.mliap.descriptor with DATE: 2014-09-05
SNAP keyword rcutfac 4.67637
SNAP keyword twojmax 6
SNAP keyword nelems 1
SNAP keyword elems Ta
SNAP keyword radelems 0.5
SNAP keyword welems 1
SNAP keyword rfac0 0.99363
SNAP keyword rmin0 0
SNAP keyword bzeroflag 0
pair_coeff 1 1 zbl ${zblz} ${zblz}
pair_coeff 1 1 zbl 73 ${zblz}
pair_coeff 1 1 zbl 73 73
pair_coeff * * mliap Ta
# Setup output
compute eatom all pe/atom
compute energy all reduce sum c_eatom
compute satom all stress/atom NULL
compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
thermo_style custom step temp epair c_energy etotal press v_press
thermo 10
thermo_modify norm yes
# Set up NVE run
timestep 0.5e-3
neighbor 1.0 bin
neigh_modify once no every 1 delay 0 check yes
# Run MD
velocity all create 300.0 4928459 loop geom
fix 1 all nve
run ${nsteps}
run 100
Neighbor list info ...
update every 1 steps, delay 0 steps, check yes
max neighbors/atom: 2000, page size: 100000
master list distance cutoff = 5.8
ghost atom cutoff = 5.8
binsize = 2.9, bins = 5 5 5
2 neighbor lists, perpetual/occasional/extra = 2 0 0
(1) pair zbl, perpetual, half/full from (2)
attributes: half, newton on
pair build: halffull/newton
stencil: none
bin: none
(2) pair mliap, perpetual
attributes: full, newton on
pair build: full/bin/atomonly
stencil: full/bin/3d
bin: standard
Per MPI rank memory allocation (min/avg/max) = 159.8 | 159.8 | 159.8 Mbytes
Step Temp E_pair c_energy TotEng Press v_press
0 300 -11.85157 -11.85157 -11.813095 2717.1661 -2717.1661
10 296.01467 -11.851059 -11.851059 -11.813095 2697.4796 -2697.4796
20 284.53666 -11.849587 -11.849587 -11.813095 2289.1527 -2289.1527
30 266.51577 -11.847275 -11.847275 -11.813095 1851.7131 -1851.7131
40 243.05007 -11.844266 -11.844266 -11.813095 1570.684 -1570.684
50 215.51032 -11.840734 -11.840734 -11.813094 1468.1899 -1468.1899
60 185.48331 -11.836883 -11.836883 -11.813094 1524.8757 -1524.8757
70 154.6736 -11.832931 -11.832931 -11.813094 1698.3351 -1698.3351
80 124.79303 -11.829099 -11.829099 -11.813094 1947.0715 -1947.0715
90 97.448054 -11.825592 -11.825592 -11.813094 2231.9563 -2231.9563
100 74.035418 -11.822589 -11.822589 -11.813094 2515.8526 -2515.8526
Loop time of 2.00236 on 48 procs for 100 steps with 128 atoms
Performance: 2.157 ns/day, 11.124 hours/ns, 49.941 timesteps/s
288.8% CPU use with 1 MPI tasks x 48 OpenMP threads
MPI task timing breakdown:
Section | min time | avg time | max time |%varavg| %total
---------------------------------------------------------------
Pair | 1.9998 | 1.9998 | 1.9998 | 0.0 | 99.87
Neigh | 0 | 0 | 0 | 0.0 | 0.00
Comm | 0.0011814 | 0.0011814 | 0.0011814 | 0.0 | 0.06
Output | 0.00059724 | 0.00059724 | 0.00059724 | 0.0 | 0.03
Modify | 0.00047352 | 0.00047352 | 0.00047352 | 0.0 | 0.02
Other | | 0.0003468 | | | 0.02
Nlocal: 128.000 ave 128 max 128 min
Histogram: 1 0 0 0 0 0 0 0 0 0
Nghost: 727.000 ave 727 max 727 min
Histogram: 1 0 0 0 0 0 0 0 0 0
Neighs: 3712.00 ave 3712 max 3712 min
Histogram: 1 0 0 0 0 0 0 0 0 0
FullNghs: 7424.00 ave 7424 max 7424 min
Histogram: 1 0 0 0 0 0 0 0 0 0
Total # of neighbors = 7424
Ave neighs/atom = 58.000000
Neighbor list builds = 0
Dangerous builds = 0
Total wall time: 0:00:03

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LAMMPS (30 Nov 2020)
using 48 OpenMP thread(s) per MPI task
# Demonstrate MLIAP/PyTorch interface to linear SNAP potential
# Initialize simulation
variable nsteps index 100
variable nrep equal 4
variable a equal 3.316
units metal
# generate the box and atom positions using a BCC lattice
variable nx equal ${nrep}
variable nx equal 4
variable ny equal ${nrep}
variable ny equal 4
variable nz equal ${nrep}
variable nz equal 4
boundary p p p
lattice bcc $a
lattice bcc 3.316
Lattice spacing in x,y,z = 3.3160000 3.3160000 3.3160000
region box block 0 ${nx} 0 ${ny} 0 ${nz}
region box block 0 4 0 ${ny} 0 ${nz}
region box block 0 4 0 4 0 ${nz}
region box block 0 4 0 4 0 4
create_box 1 box
Created orthogonal box = (0.0000000 0.0000000 0.0000000) to (13.264000 13.264000 13.264000)
1 by 2 by 2 MPI processor grid
create_atoms 1 box
Created 128 atoms
create_atoms CPU = 0.002 seconds
mass 1 180.88
# choose potential
include Ta06A.mliap.pytorch
# DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
# Definition of SNAP potential Ta_Cand06A
# Assumes 1 LAMMPS atom type
variable zblcutinner equal 4
variable zblcutouter equal 4.8
variable zblz equal 73
# Specify hybrid with SNAP, ZBL
pair_style hybrid/overlay zbl ${zblcutinner} ${zblcutouter} mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
pair_style hybrid/overlay zbl 4 ${zblcutouter} mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
pair_style hybrid/overlay zbl 4 4.8 mliap model mliappy Ta06A.mliap.pytorch.model.pkl descriptor sna Ta06A.mliap.descriptor
Loading python model complete.
Reading potential file Ta06A.mliap.descriptor with DATE: 2014-09-05
SNAP keyword rcutfac 4.67637
SNAP keyword twojmax 6
SNAP keyword nelems 1
SNAP keyword elems Ta
SNAP keyword radelems 0.5
SNAP keyword welems 1
SNAP keyword rfac0 0.99363
SNAP keyword rmin0 0
SNAP keyword bzeroflag 0
pair_coeff 1 1 zbl ${zblz} ${zblz}
pair_coeff 1 1 zbl 73 ${zblz}
pair_coeff 1 1 zbl 73 73
pair_coeff * * mliap Ta
# Setup output
compute eatom all pe/atom
compute energy all reduce sum c_eatom
compute satom all stress/atom NULL
compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
thermo_style custom step temp epair c_energy etotal press v_press
thermo 10
thermo_modify norm yes
# Set up NVE run
timestep 0.5e-3
neighbor 1.0 bin
neigh_modify once no every 1 delay 0 check yes
# Run MD
velocity all create 300.0 4928459 loop geom
fix 1 all nve
run ${nsteps}
run 100
Neighbor list info ...
update every 1 steps, delay 0 steps, check yes
max neighbors/atom: 2000, page size: 100000
master list distance cutoff = 5.8
ghost atom cutoff = 5.8
binsize = 2.9, bins = 5 5 5
2 neighbor lists, perpetual/occasional/extra = 2 0 0
(1) pair zbl, perpetual, half/full from (2)
attributes: half, newton on
pair build: halffull/newton
stencil: none
bin: none
(2) pair mliap, perpetual
attributes: full, newton on
pair build: full/bin/atomonly
stencil: full/bin/3d
bin: standard
Per MPI rank memory allocation (min/avg/max) = 159.7 | 159.7 | 159.7 Mbytes
Step Temp E_pair c_energy TotEng Press v_press
0 300 -11.85157 -11.85157 -11.813095 2717.1661 -2717.1661
10 296.01467 -11.851059 -11.851059 -11.813095 2697.4796 -2697.4796
20 284.53666 -11.849587 -11.849587 -11.813095 2289.1527 -2289.1527
30 266.51577 -11.847275 -11.847275 -11.813095 1851.7131 -1851.7131
40 243.05007 -11.844266 -11.844266 -11.813095 1570.684 -1570.684
50 215.51032 -11.840734 -11.840734 -11.813094 1468.1899 -1468.1899
60 185.48331 -11.836883 -11.836883 -11.813094 1524.8757 -1524.8757
70 154.6736 -11.832931 -11.832931 -11.813094 1698.3351 -1698.3351
80 124.79303 -11.829099 -11.829099 -11.813094 1947.0715 -1947.0715
90 97.448054 -11.825592 -11.825592 -11.813094 2231.9563 -2231.9563
100 74.035418 -11.822589 -11.822589 -11.813094 2515.8526 -2515.8526
Loop time of 0.562802 on 192 procs for 100 steps with 128 atoms
Performance: 7.676 ns/day, 3.127 hours/ns, 177.682 timesteps/s
99.7% CPU use with 4 MPI tasks x 48 OpenMP threads
MPI task timing breakdown:
Section | min time | avg time | max time |%varavg| %total
---------------------------------------------------------------
Pair | 0.53583 | 0.54622 | 0.55401 | 0.9 | 97.05
Neigh | 0 | 0 | 0 | 0.0 | 0.00
Comm | 0.0071442 | 0.01491 | 0.025289 | 5.4 | 2.65
Output | 0.00092525 | 0.00095771 | 0.0010166 | 0.0 | 0.17
Modify | 0.00014479 | 0.00015043 | 0.00015893 | 0.0 | 0.03
Other | | 0.0005624 | | | 0.10
Nlocal: 32.0000 ave 32 max 32 min
Histogram: 4 0 0 0 0 0 0 0 0 0
Nghost: 431.000 ave 431 max 431 min
Histogram: 4 0 0 0 0 0 0 0 0 0
Neighs: 928.000 ave 928 max 928 min
Histogram: 4 0 0 0 0 0 0 0 0 0
FullNghs: 1856.00 ave 1856 max 1856 min
Histogram: 4 0 0 0 0 0 0 0 0 0
Total # of neighbors = 7424
Ave neighs/atom = 58.000000
Neighbor list builds = 0
Dangerous builds = 0
Total wall time: 0:00:02

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# Demonstrate how to load a model from the python side.
# This is essentially the same as in.mliap.pytorch.Ta06A
# except that python is the driving program, and lammps
# is in library mode.
before_loading =\
"""# Demonstrate MLIAP/PyTorch interface to linear SNAP potential
# Initialize simulation
variable nsteps index 100
variable nrep equal 4
variable a equal 3.316
units metal
# generate the box and atom positions using a BCC lattice
variable nx equal ${nrep}
variable ny equal ${nrep}
variable nz equal ${nrep}
boundary p p p
lattice bcc $a
region box block 0 ${nx} 0 ${ny} 0 ${nz}
create_box 1 box
create_atoms 1 box
mass 1 180.88
# choose potential
# DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
# Definition of SNAP potential Ta_Cand06A
# Assumes 1 LAMMPS atom type
variable zblcutinner equal 4
variable zblcutouter equal 4.8
variable zblz equal 73
# Specify hybrid with SNAP, ZBL
pair_style hybrid/overlay &
zbl ${zblcutinner} ${zblcutouter} &
mliap model mliappy LATER &
descriptor sna Ta06A.mliap.descriptor
pair_coeff 1 1 zbl ${zblz} ${zblz}
pair_coeff * * mliap Ta
"""
after_loading =\
"""
# Setup output
compute eatom all pe/atom
compute energy all reduce sum c_eatom
compute satom all stress/atom NULL
compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
thermo_style custom step temp epair c_energy etotal press v_press
thermo 10
thermo_modify norm yes
# Set up NVE run
timestep 0.5e-3
neighbor 1.0 bin
neigh_modify once no every 1 delay 0 check yes
# Run MD
velocity all create 300.0 4928459 loop geom
fix 1 all nve
run ${nsteps}
"""
import lammps
lmp = lammps.lammps(cmdargs=['-echo','both'])
# Before defining the pair style, one must do the following:
import lammps.mliap
lammps.mliap.activate_mliappy(lmp)
# Otherwise, when running lammps in library mode,
# you will get an error:
# "ERROR: Loading MLIAPPY coupling module failure."
# Setup the simulation and declare an empty model
# by specifying model filename as "LATER"
lmp.commands_string(before_loading)
# Define the model however you like. In this example
# we load it from disk:
import torch
model = torch.load('Ta06A.mliap.pytorch.model.pt')
# Connect the PyTorch model to the mliap pair style.
lammps.mliap.load_model(model)
# run the simulation with the mliap pair style
lmp.commands_string(after_loading)

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# DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
# Definition of SNAP potential Ta_Cand06A
# Assumes 1 LAMMPS atom type
variable zblcutinner equal 4
variable zblcutouter equal 4.8
variable zblz equal 73
# Specify hybrid with SNAP, ZBL
pair_style hybrid/overlay &
zbl ${zblcutinner} ${zblcutouter} &
mliap model mliappy relu1hidden.mliap.pytorch.model.pt &
descriptor sna Ta06A.mliap.descriptor
pair_coeff 1 1 zbl ${zblz} ${zblz}
pair_coeff * * mliap Ta

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