Examples
1. Structure analysis
import mdapy as mp
mp.init('cpu') # use cpu, mp.init('gpu') will use gpu to compute.
system = mp.System('./CoCuFeNiPd-4M.dump') # read dump file to generate a system class
system.cal_centro_symmetry_parameter() # calculate the centrosymmetry parameters
system.cal_atomic_entropy() # calculate the atomic entropy
system.write_dump() # save results to a new dump file
2. Mean squared displacement and Lindemann index
import mdapy as mp
mp.init('cpu')
dump_list = [f'melt.{i}.dump' for i in range(100)] # obtain all the dump filenames in a list
MS = mp.MultiSystem(dump_list) # read all the dump file to generate a MultiSystem class
MS.cal_mean_squared_displacement() # calculate the mean squared displacement
MS.MSD.plot() # one can plot the MSD per frame
MS.cal_lindemann_parameter() # calculate the lindemann index
MS.Lindemann.plot() # one can plot lindemann index per frame
MS.write_dumps() # save results to a serials of dump files
3. Calculate WCP matrix in high-entropy alloy
import mdapy as mp
mp.init(arch="cpu")
system = mp.System("CoCuFeNiPd-4M.data")
system.cal_warren_cowley_parameter() # calculate WCP parameter
fig, ax = system.WarrenCowleyParameter.plot(
elements_list=["Co", "Cu", "Fe", "Ni", "Pd"]
) # plot WCP matrix
fig.savefig("WCP.png", dpi=300, bbox_inches="tight", transparent=True)
4. Create polycrystalline with graphene boundary
import mdapy as mp
import numpy as np
mp.init('cpu')
box = np.array([[0, 800.], [0, 200.], [0, 200.]]) # create a box
seednumber = 20 # create 20 seeds to generate the voronoi polygon
metal_lattice_constant = 3.615 # lattice constant of metallic matrix
metal_lattice_type = 'FCC' # lattice type of metallic matrix
randomseed = 1 # control the crystalline orientations per grains
add_graphene=True # use graphen as grain boundary
poly = mp.CreatePolycrystalline(box, seednumber, metal_lattice_constant, metal_lattice_type, randomseed=randomseed, add_graphene=add_graphene, gra_overlap_dis=1.2)
poly.compute() # generate a polycrystalline with graphene boundary
5. Calculate the EOS curve
import numpy as np
import matplotlib.pyplot as plt
import mdapy as mp
from mdapy import pltset, cm2inch
mp.init('cpu')
def get_enegy_lattice(potential, pos, box):
neigh = mp.Neighbor(pos, box, potential.rc, max_neigh=150) # build neighbor list
neigh.compute()
Cal = mp.Calculator(
potential,
pos,
[1, 1, 1],
box,
["Al"],
np.ones(pos.shape[0], dtype=np.int32),
neigh.verlet_list,
neigh.distance_list,
neigh.neighbor_number,
) # calculate the energy
Cal.compute()
return Cal.energy.mean()
eos = []
lattice_constant = 4.05
x, y, z = 3, 3, 3
FCC = mp.LatticeMaker(lattice_constant, "FCC", x, y, z) # build a FCC lattice
FCC.compute()
potential = mp.EAM("Al_DFT.eam.alloy") # read a eam.alloy potential file
for scale in np.arange(0.9, 1.15, 0.01): # loop to get different energies
energy = get_enegy_lattice(potential, FCC.pos*scale, FCC.box*scale)
eos.append([scale*lattice_constant, energy])
eos = np.array(eos)
# plot the eos results
pltset()
fig = plt.figure(figsize=(cm2inch(10), cm2inch(7)), dpi=150)
plt.subplots_adjust(bottom=0.18, top=0.92, left=0.2, right=0.98)
plt.plot(eos[:,0], eos[:,1], 'o-')
e_coh = eos[:,1].min()
a_equi = eos[np.argmin(eos[:, 1]), 0]
plt.plot([a_equi], [e_coh], 'o', mfc='white')
plt.title(r'$\mathregular{E_{Coh}}$ : %.2f eV, a : %.2f $\mathregular{\AA}$' % (e_coh, a_equi), fontsize=10)
plt.xlim(eos[0,0]-0.2, eos[-1,0]+0.2)
plt.xlabel("a ($\mathregular{\AA}$)")
plt.ylabel(r"PE (eV/atom)")
ax = plt.gca()
plt.savefig('eos.png', dpi=300, bbox_inches='tight', transparent=True)
plt.show()
6. Collaborative use with Ovito
This function can run in script environment of Ovito.
from ovito.data import *
import mdapy as mp
import numpy as np
mp.init()
def modify(frame, data):
yield "Transforming mdapy system..."
cell_ovito = data.cell[...]
box = np.r_[cell_ovito[:,:-1], np.expand_dims(cell_ovito[:,-1],axis=0)]
pos = np.array(data.particles['Position'][...])
boundary = [int(i) for i in data.cell.pbc]
system = mp.System(pos=pos, box=box, boundary=boundary)
yield "Performing compute entropy..."
system.cal_atomic_entropy(
rc=3.6*1.4,
sigma=0.2,
use_local_density=True,
compute_average=True,
average_rc=3.6*0.9,
max_neigh=80,
)
data.particles_.create_property("entropy", data=system.data['ave_atomic_entropy'].to_numpy())
yield "Performing compute CNP..."
system.cal_common_neighbor_parameter(rc=3.6*0.8536)
data.particles_.create_property("cnp", data=system.data['cnp'].to_numpy())
7. Identify stacking faults (SFs) and twin boundaries (TBs) in Ovito
This function can run in script environment of Ovito. (Tested in Ovito 3.0.0-dev581). Here the stacking faults include intrinsic SFs and multi layer SFs.
from ovito.data import *
from ovito.modifiers import ExpressionSelectionModifier, AssignColorModifier
import mdapy as mp
import numpy as np
mp.init()
def modify(
frame,
data,
other=(243 / 255, 243 / 255, 243 / 255),
fcc=(102 / 255, 255 / 255, 102 / 255),
bcc=(102 / 255, 102 / 255, 255 / 255),
hcp=(255/255, 85/255, 0/255),
ISF=(0.9, 0.7, 0.2),
ESF=(132/255, 25/255, 255/255),
TB=(255 / 255, 102 / 255, 102 / 255),
):
yield "Transforming mdapy system..."
cell_ovito = data.cell[...]
box = np.r_[cell_ovito[:,:-1], np.expand_dims(cell_ovito[:,-1],axis=0)]
pos = np.array(data.particles["Position"][...])
boundary = [int(i) for i in data.cell.pbc]
system = mp.System(pos=pos, box=box, boundary=boundary)
yield "Performing identify SFTB..."
system.cal_identify_SFs_TBs()
data.particles_.create_property(
"structure_types", data=system.data["structure_types"].to_numpy()
)
data.particles_.create_property(
"fault_types", data=system.data["fault_types"].to_numpy()
)
yield "Coloring atoms..."
data.apply(
ExpressionSelectionModifier(expression="structure_types==0 || fault_types==1")
)
data.apply(AssignColorModifier(color=other))
data.apply(ExpressionSelectionModifier(expression="structure_types==1"))
data.apply(AssignColorModifier(color=fcc))
data.apply(ExpressionSelectionModifier(expression="structure_types==3"))
data.apply(AssignColorModifier(color=bcc))
data.apply(ExpressionSelectionModifier(expression="fault_types==4"))
data.apply(AssignColorModifier(color=hcp))
data.apply(ExpressionSelectionModifier(expression="fault_types==2"))
data.apply(AssignColorModifier(color=ISF))
data.apply(ExpressionSelectionModifier(expression="fault_types==5"))
data.apply(AssignColorModifier(color=ESF))
data.apply(ExpressionSelectionModifier(expression="fault_types==3"))
data.apply(AssignColorModifier(color=TB))
data.apply(ExpressionSelectionModifier(expression="structure_types==10"))
