102 lines
2.3 KiB
Python
102 lines
2.3 KiB
Python
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 numpy as np
|
|
|
|
class LinearModel():
|
|
def __init__(self,file):
|
|
coeffs = np.genfromtxt(file,skip_header=6)
|
|
self.bias = coeffs[0]
|
|
self.weights = coeffs[1:]
|
|
self.n_params = len(coeffs)
|
|
self.n_descriptors = len(self.weights)
|
|
self.n_elements = 1
|
|
def __call__(self,elems,bispectrum,beta,energy):
|
|
energy[:] = bispectrum @ self.weights + self.bias
|
|
beta[:] = self.weights
|
|
|
|
mymodel = LinearModel("Ta06A.mliap.model")
|
|
|
|
import lammps
|
|
|
|
lmp = lammps.lammps(cmdargs=['-echo','both'])
|
|
|
|
import lammps.mliap
|
|
lammps.mliap.activate_mliappy(lmp)
|
|
lmp.commands_string(before_loading)
|
|
lammps.mliap.load_model(mymodel)
|
|
lmp.commands_string(after_loading)
|
|
lmp.close()
|
|
lmp.finalize()
|