98 lines
2.3 KiB
Python
98 lines
2.3 KiB
Python
# Demonstrate how to load a model from the python side.
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# This is essentially the same as in.mliap.pytorch.MOF
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# except that python is the driving program, and lammps
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# is in library mode.
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before_loading =\
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"""# Demonstrate MLIAP/PyTorch interface to torch model
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# Initialize simulation
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variable nsteps index 100
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variable nrep equal 4
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variable a equal 3.316
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units metal
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# generate the box and atom positions using a BCC lattice
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variable nx equal ${nrep}
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variable ny equal ${nrep}
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variable nz equal ${nrep}
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boundary p p p
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lattice bcc $a
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region box block 0 ${nx} 0 ${ny} 0 ${nz}
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create_box 1 box
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create_atoms 1 box
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mass 1 180.88
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# define potential with LATER mliappy
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pair_style mliap model mliappy LATER descriptor ace ccs_single_element.yace
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pair_coeff * * Ta
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"""
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after_loading =\
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"""
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# Setup output
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compute eatom all pe/atom
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compute energy all reduce sum c_eatom
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compute satom all stress/atom NULL
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compute str all reduce sum c_satom[1] c_satom[2] c_satom[3]
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variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
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thermo_style custom step temp epair c_energy etotal press v_press
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thermo 10
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thermo_modify norm yes
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# Set up NVE run
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dump 1 all cfg 10 ats.*.cfg mass type xs ys zs
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dump_modify 1 element Ta
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timestep 0.5e-3
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neighbor 1.0 bin
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neigh_modify once no every 1 delay 0 check yes
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# Run MD
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velocity all create 300.0 4928459 loop geom
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fix 1 all nve
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run ${nsteps}
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"""
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import lammps
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lmp = lammps.lammps(cmdargs=['-echo','both'])
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# Before defining the pair style, one must do the following:
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import lammps.mliap
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lammps.mliap.activate_mliappy(lmp)
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# Otherwise, when running lammps in library mode,
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# you will get an error:
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# "ERROR: Loading MLIAPPY coupling module failure."
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# Setup the simulation and declare an empty model
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# by specifying model filename as "LATER"
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lmp.commands_string(before_loading)
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# Define the model however you like. In this example
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# we load it from disk:
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import os
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import torch
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torch_model = 'ACE_NN_Pytorch.pt'
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if not os.path.exists(torch_model):
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raise FileNotFoundError(f"Generate {torch_model} first")
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model = torch.load(torch_model)
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# Connect the PyTorch model to the mliap pair style.
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lammps.mliap.load_model(model)
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# run the simulation with the mliap pair style
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lmp.commands_string(after_loading)
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lmp.close()
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lmp.finalize()
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