update unified LJ Ar example to include creating the pickle in the input
This commit is contained in:
Axel Kohlmeyer
@ -1,6 +1,6 @@
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This directory contains multiple examples of
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machine-learning potentials defined using the
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MLIAP package in LAMMPS. The input files
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This directory contains multiple examples of
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machine-learning potentials defined using the
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ML-IAP package in LAMMPS. The input files
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are described below.
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in.mliap.snap.Ta06A
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@ -21,14 +21,14 @@ Run EME-SNAP, equivalent to examples/snap/in.snap.InP.JCPA2020
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in.mliap.snap.compute
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---------------------
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Generate the A matrix, the gradients (w.r.t. coefficients)
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of total potential energy, forces, and stress tensor for
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Generate the A matrix, the gradients (w.r.t. coefficients)
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of total potential energy, forces, and stress tensor for
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linear SNAP, equivalent to in.snap.compute
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in.mliap.quadratic.compute
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--------------------------
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Generate the A matrix, the gradients (w.r.t. coefficients)
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of total potential energy, forces, and stress tensor for
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Generate the A matrix, the gradients (w.r.t. coefficients)
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of total potential energy, forces, and stress tensor for
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for quadratic SNAP, equivalent to in.snap.compute.quadratic
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in.mliap.pytorch.Ta06A
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@ -41,8 +41,8 @@ This example can be run in two different ways:
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1: Running a LAMMPS executable: in.mliap.pytorch.Ta06A
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First run ``python convert_mliap_Ta06A.py``. It creates
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a PyTorch energy model that replicates the
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SNAP Ta06A potential and saves it in the file
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a PyTorch energy model that replicates the
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SNAP Ta06A potential and saves it in the file
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"Ta06A.mliap.pytorch.model.pt".
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You can then run the example as follows
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@ -52,7 +52,7 @@ You can then run the example as follows
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The resultant log.lammps output should be identical to that generated
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by in.mliap.snap.Ta06A.
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If this fails, see the instructions for building the MLIAP package
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If this fails, see the instructions for building the ML-IAP package
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with Python support enabled. Also, confirm that the
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LAMMPS Python embedded Python interpreter is
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working by running ../examples/in.python.
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@ -62,7 +62,7 @@ working by running ../examples/in.python.
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Before testing this, ensure that the previous method
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(running a LAMMPS executable) works.
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You can run the example in serial:
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You can run the example in serial:
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`python mliap_pytorch_Ta06A.py`
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@ -80,25 +80,25 @@ the script will exit with an error message.
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in.mliap.pytorch.relu1hidden
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----------------------------
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This example demonstrates a simple neural network potential
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using PyTorch and SNAP descriptors.
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using PyTorch and SNAP descriptors.
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`lmp -in in.mliap.pytorch.relu1hidden -echo both`
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It was trained on just the energy component (no forces) of
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the data used in the original SNAP Ta06A potential for
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tantalum (Thompson, Swiler, Trott, Foiles, Tucker,
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It was trained on just the energy component (no forces) of
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the data used in the original SNAP Ta06A potential for
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tantalum (Thompson, Swiler, Trott, Foiles, Tucker,
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J Comp Phys, 285, 316 (2015).). Because of the very small amount
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of energy training data, it uses just 1 hidden layer with
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a ReLU activation function. It is not expected to be
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very accurate for forces.
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of energy training data, it uses just 1 hidden layer with
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a ReLU activation function. It is not expected to be
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very accurate for forces.
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NOTE: Unlike the previous example, this example uses
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a pre-built PyTorch file `Ta06A.mliap.pytorch.model.pt`.
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NOTE: Unlike the previous example, this example uses
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a pre-built PyTorch file `Ta06A.mliap.pytorch.model.pt`.
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It is read using `torch.load`,
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which implicitly uses the Python `pickle` module.
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This is known to be insecure. It is possible to construct malicious
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pickle data that will execute arbitrary code during unpickling. Never
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load data that could have come from an untrusted source, or that
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This is known to be insecure. It is possible to construct malicious
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pickle data that will execute arbitrary code during unpickling. Never
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load data that could have come from an untrusted source, or that
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could have been tampered with. Only load data you trust.
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in.mliap.nn.Ta06A
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@ -112,36 +112,36 @@ Run a neural network potential for Cu, a combination of SNAP descriptors and the
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in.mliap.unified.lj.Ar
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-----------------------
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This reproduces the output of examples/melt/in.melt,
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but using the ``unified`` keyword and
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but using the ``unified`` keyword and
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the Python coupling to PyTorch.
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This example can be run in two different ways:
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1: Running a LAMMPS executable: in.mliap.unified.lj.Ar
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First run ``python ./pickle_mliap_unified_lj_Ar.py``. It creates
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an MLIAP unified model that replicates the ``lj/cut`` pair
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style defined in examples/melt/in/melt
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and saves it in the file "mliap_unified_lj_Ar.pkl".
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You can then run the example as follows
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You can run the example as follows
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`lmp -in in.mliap.unified.lj.Ar -echo both`
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The resultant log.lammps output should be identical to that generated
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by in.melt.
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If this fails, see the instructions for building the MLIAP package
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with Python support enabled. Also, confirm that the
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LAMMPS Python embedded Python interpreter is
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working by running ../examples/in.python.
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The input first runs ``python pickle_mliap_unified_lj_Ar.py`` and
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thus creates an ML-IAP unified model that replicates the ``lj/cut``
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pair style defined in examples/melt/in/melt and saves it in the
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file "mliap_unified_lj_Ar.pkl".
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If this fails, see the instructions for building the ML-IAP package
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with Python support enabled. Also, confirm that the LAMMPS Python
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embedded Python interpreter is working by running ../examples/in.python
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and that the LAMMPS python module is installed and working.
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2: Running a Python script: mliap_unified_lj_Ar.py
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Before testing this, ensure that the previous method
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(running a LAMMPS executable) works.
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You can run the example in serial:
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You can run the example in serial:
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`python mliap_unified_lj_Ar.py`
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@ -155,17 +155,17 @@ by in.melt and in.mliap.unified.lj.Ar.
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in.mliap.unified.hippynn.Al
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---------------------------
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Demonstrate MLIAP interface to HIPNN Al potential. Requires the HIPNN code.
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Demonstrate ML-IAP interface to HIPNN Al potential. Requires the HIPNN code.
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in.mliap.unified.hippynn.Al.ghostneigh
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--------------------------------------
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Demonstrate MLIAP interface to HIPNN Al potential with ghost neighbors. Requires the HIPNN code.
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Demonstrate ML-IAP interface to HIPNN Al potential with ghost neighbors. Requires the HIPNN code.
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in.mliap.unified.hippynn.InP
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----------------------------
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Demonstrate MLIAP interface to HIPNN InP potential. Requires the HIPNN code.
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Demonstrate ML-IAP interface to HIPNN InP potential. Requires the HIPNN code.
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in.mliap.so3.Ni_Mo
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------------------
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@ -1,35 +1,44 @@
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# create pickle of unified model
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shell rm mliap_unified_lj_Ar.pkl
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shell python pickle_mliap_unified_lj_Ar.py
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# 3d Lennard-Jones melt
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units lj
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atom_style atomic
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units lj
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atom_style atomic
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lattice fcc 0.8442
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region box block 0 10 0 10 0 10
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create_box 1 box
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create_atoms 1 box
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mass 1 1.0
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lattice fcc 0.8442
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region box block 0 10 0 10 0 10
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create_box 1 box
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create_atoms 1 box
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mass 1 1.0
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velocity all create 3.0 87287 loop geom
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velocity all create 3.0 87287 loop geom
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pair_style mliap unified mliap_unified_lj_Ar.pkl
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pair_coeff * * Ar
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pair_style mliap unified mliap_unified_lj_Ar.pkl
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pair_coeff * * Ar
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neighbor 0.3 bin
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neigh_modify every 20 delay 0 check no
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neighbor 0.3 bin
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neigh_modify every 20 delay 0 check no
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fix 1 all nve
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fix 1 all nve
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#dump id all atom 50 dump.melt
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#dump id all atom 50 dump.melt
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#dump 2 all image 25 image.*.jpg type type &
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# axes yes 0.8 0.02 view 60 -30
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#dump_modify 2 pad 3
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#dump 2 all image 25 image.*.jpg type type &
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# axes yes 0.8 0.02 view 60 -30
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#dump_modify 2 pad 3
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#dump 3 all movie 1 movie.mpg type type &
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# axes yes 0.8 0.02 view 60 -30
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#dump_modify 3 pad 3
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#dump 3 all movie 1 movie.mpg type type &
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# axes yes 0.8 0.02 view 60 -30
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#dump_modify 3 pad 3
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#dump 4 all custom 1 forces.xyz fx fy fz
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thermo 50
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run 250
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thermo 50
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run 250
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# clean up
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shell rm mliap_unified_lj_Ar.pkl
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