107 lines
2.4 KiB
Python
107 lines
2.4 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.Ta06A
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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 linear SNAP potential
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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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# choose potential
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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)
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# Definition of SNAP potential Ta_Cand06A
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# Assumes 1 LAMMPS atom type
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variable zblcutinner equal 4
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variable zblcutouter equal 4.8
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variable zblz equal 73
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# Specify hybrid with SNAP, ZBL
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pair_style hybrid/overlay &
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zbl ${zblcutinner} ${zblcutouter} &
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mliap model mliappy LATER &
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descriptor sna Ta06A.mliap.descriptor
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pair_coeff 1 1 zbl ${zblz} ${zblz}
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pair_coeff * * mliap 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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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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# this commmand must be run before the MLIAP object is declared in lammps.
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lmp.mliappy.activate()
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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 PyTorch model by loading a pkl file.
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# this could also be done in other ways.
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import pickle
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with open('Ta06A.mliap.pytorch.model.pkl','rb') as pfile:
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model = pickle.load(pfile)
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# connect the PyTorch model to the mliap pair style
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lmp.mliappy.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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