def get_fracs(metals=None, shape=None, num_shells=None, return_ee=False,
x_metal1=None, **kwargs):
res = db_inter.get_bimet_result(metals=metals, shape=shape,
num_shells=num_shells, **kwargs)
# limit by composition
if x_metal1 is not None:
res = filter(lambda r: abs(r.n_metal1 /
r.num_atoms - x_metal1) <= 0.05,
res)
res = sorted(res, key=lambda r: [r.shape, r.num_atoms])
fracs = np.zeros((len(res), 3))
ee = np.zeros(len(res))
# track shape and size so if they don't change, you don't reload atomgraph
prevsize = res[0].num_atoms
prevshape = res[0].shape
# load the first atomgraph
bonds = res[0].nanoparticle.load_bonds_list()
ag = atomgraph.AtomGraph(bonds, res[0].metal1, res[0].metal2)
for i, a in enumerate(res):
# only create new atomgraph for new size and/or shape
if a.num_atoms != prevsize or a.shape != prevshape:
bonds = a.nanoparticle.load_bonds_list()
ag = atomgraph.AtomGraph(bonds, a.metal1, a.metal2)
# update previous size and shape
prevsize = a.num_atoms
prevshape = a.shape
# get bond type counts
counts = ag.countMixing(np.array([int(z) for z in a.ordering]))
# normalize bond counts to get bond fractions
fracs[i] = counts / counts.sum()
ee[i] = a.EE
# return bond fraction for metal1-metal1 (aa) and metal2-metal2 (bb)
aa = fracs[:, 0]
bb = fracs[:, 1]
# option to return excess energy with bond fractions
if return_ee:
return aa, bb, ee
return aa, bb
def test_get_bimet_result__return_metals_matches():
# get 3 random metal pairs found in BimetallicResults table
metals = random.sample(set(db_inter.build_metal_pairs_list()), 3)
for m in metals:
res = db_inter.get_bimet_result(metals=m, lim=5, return_list=True)
assert all(r.metal1 == m[0] and r.metal2 == m[1] for r in res)
def test_get_bimet_result__return_n_metal1_matches():
for n1 in [30, 40]:
res = db_inter.get_bimet_result(n_metal1=n1, lim=5, return_list=True)
assert all(r.n_metal1 == n1 for r in res)
def test_get_bimet_result__return_only_bimet_results():
res = db_inter.get_bimet_result(only_bimet=True, lim=20, return_list=True)
assert all(0 < r.n_metal1 < r.num_atoms for r in res)
def test_get_bimet_result__return_num_atoms_matches():
for n in [13, 55]:
res = db_inter.get_bimet_result(num_atoms=n, lim=5, return_list=True)
assert all(r.num_atoms == n for r in res)
def test_get_bimet_result__return_num_shells_matches():
for s in [1, 2]:
res = db_inter.get_bimet_result(num_shells=s, lim=5, return_list=True)
assert all(r.nanoparticle.num_shells == s for r in res)
from ce_expansion.npdb import db_inter
from pprint import pprint
from ce_expansion.atomgraph.bcm import BCModel
import numpy as np
import os
from pprint import pprint
import ase.units as units
this_dir = os.path.dirname(os.path.abspath(__file__))
res = db_inter.get_bimet_result('AuCu', 'icosahedron', 55, n_metal1=22)
atoms = res.atoms_obj
bond_list = res.nanoparticle.bonds_list
metal_types = ['Cu', 'Au', "Ag"]
# STEP 1
# create a BCModel object
#TODO: account for this case in bcm
#metal_types = ['Cu', 'Au', 'au', 'ag']
bcm = BCModel(atoms, bond_list, metal_types)
assert bcm.metal_types == ['Ag', 'Au', 'Cu']
print("STEP 1")
# STEP 2
# make sure everything passed in becomes an attribute
print(bcm.atoms)
def test_get_bimet_result__return_shape_matches():
for shape in ['icosahedron', 'cuboctahedron']:
res = db_inter.get_bimet_result(shape=shape, lim=5, return_list=True)
assert all(r.shape == shape for r in res)
n_ag = n
x_ag = n / num_atoms
n_au = num_atoms - n
x_au = n_au / num_atoms
if n == 0:
bcenergy = au_en
laenergy = lars_au_en
elif n == 309:
bcenergy = ag_en
laenergy = lars_ag_en
else:
# our bimet NP
res = db_inter.get_bimet_result('agau', 'icosahedron',
num_atoms=num_atoms,
n_metal1=n, lim=1)
atom = res.build_atoms_obj()
atom = ase.Atoms(atom)
atom.set_calculator(emt.EMT())
opt = ase.optimize.BFGS(atom)
opt.run()
bcenergy = atom.get_total_energy()
save_bc(atom)
# Larson's minimum bimet NP
larson = ase.io.read(os.path.join(np_ga, 'larson_orig_structures',
'%s.xyz' % atom.get_chemical_formula()))
larson = ase.Atoms(larson)
larson.set_calculator(emt.EMT())
opt = ase.optimize.BFGS(larson)
ax.set_yticklabels(['{:,.0%}'.format(x)
for x in ax.get_yticks()[:-1]] + [''])
else:
ax.set_ylabel('Number of Atoms')
ax.set_ylim(0, max(cn_dist['tot_counts']) * 1.1)
ax.bar(x, cn_dist['m1_counts'], color=m1_color, edgecolor='k',
label=m1)
ax.bar(x, cn_dist['m2_counts'], bottom=cn_dist['m1_counts'],
color=m2_color, edgecolor='k', label=m2)
ax.legend()
fig.tight_layout()
if show:
plt.show()
return fig, ax
if __name__ == '__main__':
metals = 'agau'
shape = 'icosahedron'
num_shells = 9
bonds = np.array(structure_gen.build_structure_sql(shape, num_shells)
.bonds_list)
res = db_inter.get_bimet_result(metals, shape=shape, num_shells=num_shells,
only_bimet=True)[4]
fig, ax = cn_dist_plot(res, pcty=True)
plt.show()
minn = 1 if shape.startswith('cub') else 2
# number of shells on inside and outside to ignore in calculation
buffer = 3
for s in range(2 * buffer + 1, 11):
if shape.startswith('cub'):
s -= 1
# get number of atoms
n = db_inter.get_shell2num(shape, s)
# get half such that metal1 has the extra atom (if natoms is odd)
n = (n + n % 2) // 2
res = db_inter.get_bimet_result(metals, shape=shape, num_shells=s,
n_metal1=n)
# get ordering array
ordering = np.array([int(i) for i in res.ordering])
# load bonds list
bonds = res.nanoparticle.load_bonds_list()
# build atomgraph object
ag = atomgraph.AtomGraph(bonds, 'Au', 'Cu')
# get atom indices for each shell
shells = db_inter.build_atoms_in_shell_dict(shape, s)
# create a 'Test Atom' to ensure shells are being correctly counted
test_atom = res.build_atoms_obj()
def check_db_values(update_db=False, metal_opts=None):
"""
Checks CE values in database to ensure CE and EE match their ordering
KArgs:
- update_db (bool): if True and mismatch is found, the database will be
updated to the correct CE and EE
(Default: False)
- metal_opts (list): can pass in list of metal combination options to check
(Default: all metal pairs from results in DB)
Returns:
- (np.ndarray): CE's and EE's of mismatches found
- each row contains: [CE-db, CE-actual, EE-db, EE-actual]
"""
# if None, get all metal pairs found in BimetallicResults table in DB
if metal_opts is None:
metal_opts = db_inter.build_metal_pairs_list()
# track systems that failed test
fails = []
# get all nanoparticle (shape, num_shell) pairs in database
nanoparticles = set([(r.shape, r.num_shells)
for r in db_inter.get_nanoparticle()])
# tracked number of results checked
num_checked = 0
print('Checking results...')
for shape, num_shells in nanoparticles:
nanop = ga.structure_gen.build_structure_sql(shape, num_shells)
for metals in metal_opts:
# ensure metal types are sorted
metals = sorted(metals)
# find all bimetallic results matching shape, size, and metals
results = db_inter.get_bimet_result(metals,
shape=shape,
num_shells=num_shells)
# if no results found, continue to next metal combination
if not results:
continue
# else create bcmraph object
bcm = atomgraph.AtomGraph(nanop.bonds_list, metals[0], metals[1])
# iterate over results to compare CE in databse vs.
# CE calculated with ordering
for res in results:
ordering = np.array(list(map(int, res.ordering)))
n_metal2 = ordering.sum()
actual_ce = bcm.getTotalCE(ordering)
actual_ee = bcm.getEE(ordering)
# increment number of results checkered
num_checked += 1
# create output string
outp = (f'{res.shape[:3].upper():>4}', f'{res.num_atoms:<7,}',
f'{res.build_chem_formula():<15}')
# if deviation, add info to fails list
if abs(actual_ce - res.CE) > 1E-10:
# print system with problem
print(*outp, f'WRONG VALUE! ({actual_ce - res.CE:.3e} eV)')
fails.append([
metals, shape, num_shells, n_metal2, res.CE, actual_ce,
res.EE, actual_ee
])
# if update_db, correct the CE value
# NOTE: this most likely means that CE is not optimized
if update_db:
db_inter.update_bimet_result(metals=metals,
shape=res.shape,
num_atoms=res.num_atoms,
diameter=res.diameter,
n_metal1=res.n_metal1,
CE=actual_ce,
ordering=res.ordering,
EE=actual_ee,
nanop=res.nanoparticle,
allow_insert=False,
ensure_ce_min=False)
else:
print(*outp, end='\r')
fails = np.array(fails)
nfail = len(fails)
issue_str = 'issue' if nfail == 1 else 'issues'
print(' ' * 50, end='\r')
print(f'{nfail:,} {issue_str} found.')
print(f'{num_checked:,} results checked.')
return fails
def build_nmet2_nmet2shell_plot(metals,
shape,
num_shells,
show_ee=True,
show=True,
save=False,
pctx=False,
pcty=False):
"""Plots number of metal2 in shell_i vs number of metal2 in NP
Args:
- metals (str): two metals (element names) in NP
- shape (str): shape of NP
CURRENTLY SUPPORTS:
- cuboctahedron
- elongated-pentagonal-bipyramid
- icosahedron
- num_shells (int): number of shells the NP is made of
(excluding core atom)
KArgs:
show_ee (bool): plots excess energy (EE) on same plot with secondary axis
(default: {True})
- show (bool): shows figure is True
(default: True)
- save (bool): saves figure is True
(default: False)
- pctx (bool): x-axis plotted as concentration of metal2 if True
(default: {False})
- pcty (bool): y-axis plotted as percentage of shell_i that is metal2
(default: {False})
Returns:
- (plt.Figure): matplotlib plot of results
- (np.ndarray): matrix of results: [n_metal2, (n|x)metal2-shell_i, ..., EE]
- (dict): minimum CE and minimum EE NP as datatable.BimetallicResults objs
Raises:
ValueError: num_shells must be greater than 0
"""
if num_shells < 1:
raise ValueError("num_shells is too small")
# build dictionary of atom shell indices
shell_dict = db_inter.build_atoms_in_shell_dict(shape, num_shells)
# if pct use X else N
xtype = 'X' if pctx else 'N'
ytype = 'X' if pcty else 'N'
# all results run with specific size, shape, and metals
nanoparticles = db_inter.get_bimet_result(metals=metals,
shape=shape,
num_shells=num_shells)
# get NP with lowest Cohesive Energy (CE) and Excess Energy (EE)
min_atoms = {
'CE': min(nanoparticles, key=lambda i: i.CE),
'EE': min(nanoparticles, key=lambda i: i.EE)
}
# initialize results matrix
results = np.zeros((len(nanoparticles), len(shell_dict) + 2))
# record counts in results matrix
for i, nanop in enumerate(nanoparticles):
results[i, 0] = nanop.n_metal2
for s in shell_dict:
col = s + 1
# add number of metal2 in shell <s> to results
results[i, col] = (nanop.build_atoms_obj()[shell_dict[s]].symbols
== nanop.metal2).sum()
# calculate as percentage of atoms that are "metal2" in given shell
if pcty:
results[i, col] = results[i, col] / len(shell_dict[s])
# record EE of nanoparticle
results[i, -1] = nanop.EE
# sort results by n_metal2
results = results[results[:, 0].argsort()]
# convert x axis to metal 2 concentration if pctx
if pctx:
results[:, 0] = results[:, 0] / nanop.num_atoms
fig, ax = plt.subplots(figsize=(9, 7))
plt.rcParams['axes.spines.right'] = show_ee
if show_ee:
# excess energy secondary axis
ee_color = 'dodgerblue'
ax2 = ax.twinx()
ax2.tick_params(axis='y', labelcolor=ee_color)
ax.scatter([], [],
marker='o',
label='EE',
color=ee_color,
s=50,
edgecolor='k')
ax2.scatter(results[:, 0],
results[:, -1],
marker='o',
color=ee_color,
edgecolor='k',
s=50)
ax2.set_ylabel('Excess Energy (eV / atom)', color=ee_color)
# set ylim
mag = (results[:, -1].max() - results[:, -1].min())
buffmin = mag * 0.1
buffmax = mag * 0.3
ax2.set_ylim(results[:, -1].min() - buffmin,
results[:, -1].max() + buffmax)
# plot (n or x)i vs (n or x)i-shell
for i, col in enumerate(range(results.shape[1] - 2, 0, -1)):
# outer shell = Surface
if not i:
lab = 'Surface'
# DO NOT PLOT CORE ATOM (ignore it)
elif col == 1:
lab = 'Core'
continue
else:
lab = 'Shell %i' % (col - 1)
ax.plot(results[:, 0], results[:, col], 'o-', label=lab, zorder=col)
if pctx:
ax.set_xlim(-0.1, 1.1)
if pcty:
ax.set_ylim(0, 1.19)
ax.set_yticklabels(['{:,.0%}'.format(x) for x in ax.get_yticks()])
ax.set_xlabel('$\\rm %s_{%s}$ in NP' % (xtype, nanop.metal2))
ax.set_ylabel('$\\rm %s_{%s}$ in Shell$\\rm _i$' % (ytype, nanop.metal2))
title = '%s atom %s%s %s NP' % (nanop.num_atoms, nanop.metal1,
nanop.metal2, nanop.shape)
ax.set_title(title)
ax.legend(loc='upper center', ncol=4, handletextpad=0.2, columnspacing=0.6)
fig.tight_layout()
user = os.path.expanduser('~')
path = os.path.join(
box, 'Michael_Cowan_PhD_research', 'np_ga', 'FIGURES',
'%smet_vs_%smetshell-LARSON\\%s%s-%s' %
(xtype, ytype, nanop.metal1, nanop.metal2, nanop.shape[:3]))
if save:
if not os.path.isdir(path):
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
if show_ee:
path = os.path.join(path, 'ee')
if not os.path.isdir(path):
os.mkdir(path)
fig.savefig(os.path.join(path,
title.replace(' ', '_') + '.png'),
dpi=50)
fig.savefig(os.path.join(path, title.replace(' ', '_') + '.svg'))
res = min_atoms['EE']
res.save_np(
os.path.join(
path,
'EE-%s-%s.xyz' % (res.build_chem_formula(), res.shape[:3])))
if show:
plt.show()
return fig, results, min_atoms
def batch_run_ga_from_ga_module__needs_fixing(metals,
shape,
save_data=True,
batch_runinfo=None,
shells=None,
max_generations=5000,
min_generations=-1,
max_nochange=2000,
add_coreshell=True,
**kwargs):
"""
Submission function to run GAs of a given metal combination and
shape, sweeping over different sizes (measured in number of shells)
- capable of saving minimum structures as XYZs, logging GA stats, saving
all run info as excel, and creating a 3D surface plot of results
Args:
- metals (iterator): list of two metals used in the bimetallic NP
- shape (str): shape of NP that is being studied
NOTE: currently supports
- icosahedron
- cuboctahedron
- fcc-cube
- elongated-trigonal-pyramic
KArgs:
- plotit (bool): if true, a 3D surface plot is made of GA sims
dope concentration vs. size vs. excess energy
(Default: False)
- save_data (bool): - if true, GA sim data is saved to BimetallicResults
table in database
(Default: True)
- batch_runinfo (str): if str, add to BimetallicLog entry
(Default: None)
- shells (int || list): if int, only that shell size is simulated
elif list of ints, nshell_range = shells
(Default: None)
- max_generations (int): if not -1, use specified value as max
generations for each GA sim
(Default: 5000)
- min_generations (int): if not -1, use specified value as min
generations that run before max_nochange
criteria is applied
(Default: -1)
- max_nochange (int): maximum generations GA will go without a change in
minimum CE
(Default: 2000)
- add_coreshell (bool): if True, core shell structures will be included in
GA simulations
(Default: True)
Returns: None
"""
# track start of run
start_time = dt.now()
# clear previous plots and define desktop and data paths
plt.close('all')
desk = os.path.join(os.path.expanduser('~'), 'desktop')
# number of shells range to sim ga for each shape
default_shell_range = [1, 11]
shape2shell = {
'cuboctahedron': default_shell_range,
'elongated-pentagonal-bipyramid': default_shell_range,
'fcc-cube': default_shell_range,
'icosahedron': default_shell_range
}
nshell_range = shape2shell[shape]
if shells:
if isinstance(shells, int):
nshell_range = [shells, shells + 1]
else:
nshell_range = shells
nstructs = len(range(*nshell_range))
if not nstructs:
print(nshell_range)
return
# print run info
print('')
print('RUN INFO'.center(CENTER, '-'))
print(' Metals: %s' % (' '.join(metals_types)))
print(' Shape: %s' % shape)
print(' Save GA Results: %s' % bool(save_data))
print(' Shell Range: %s' % str(nshell_range))
print('-' * CENTER)
# keep track of total new minimum CE structs (based on saved structs)
tot_new_structs = 0
# count total structs
tot_structs = 0
for struct_i, nshells in enumerate(range(*nshell_range)):
# build atom, adjacency list, and AtomGraph
nanop = structure_gen.build_structure_sql(shape,
nshells,
build_bonds_arr=True)
num_atoms = len(nanop)
diameter = nanop.get_diameter()
BCModel(nanop.atoms, nanop.bonds_list, metal_types)
ag = BCModel(nanop.bonds_list, metal1, metal2)
# check to see if monometallic results exist
# if not, calculate them
mono1 = db_inter.get_bimet_result(metals=metals,
shape=shape,
num_atoms=num_atoms,
n_metal1=num_atoms,
lim=1)
if not mono1:
mono_ord = np.zeros(num_atoms)
mono_ce1 = ag.getTotalCE(mono_ord)
mono1 = db_inter.update_bimet_result(
metals=metals,
shape=shape,
num_atoms=num_atoms,
diameter=diameter,
n_metal1=num_atoms,
CE=mono_ce1,
ordering=''.join(str(int(i)) for i in mono_ord),
EE=0,
nanop=nanop,
allow_insert=True)
mono2 = db_inter.get_bimet_result(metals=metals,
shape=shape,
num_atoms=num_atoms,
n_metal1=0,
lim=1)
if not mono2:
mono_ord = np.ones(num_atoms)
mono_ce2 = ag.getTotalCE(mono_ord)
mono2 = db_inter.update_bimet_result(
metals=metals,
shape=shape,
num_atoms=num_atoms,
diameter=diameter,
n_metal1=0,
CE=mono_ce2,
ordering=''.join(str(int(i)) for i in mono_ord),
EE=0,
nanop=nanop,
allow_insert=True)
# USE THIS TO TEST EVERY CONCENTRATION
if nanop.num_atoms < 366:
n = np.arange(0, num_atoms + 1)
x = n / float(num_atoms)
else:
# x = metal2 concentration [0, 1]
x = np.linspace(0, 1, 11)
n = (x * num_atoms).astype(int)
# recalc concentration to match n
x = n / float(num_atoms)
# add core-shell structures list of comps to run
if add_coreshell:
srfatoms = db_inter.build_atoms_in_shell_dict(shape, nshells)
nsrf = len(srfatoms)
ncore = num_atoms - nsrf
n = np.unique(n.tolist() + [ncore, nsrf])
x = n / float(num_atoms)
# total structures checked ( - 2 to exclude monometallics)
tot_structs += float(len(n) - 2)
starting_outp = '%s%s in %i atom %s' % (metal1, metal2, num_atoms,
shape)
print(starting_outp.center(CENTER))
# track min structures for each size
new_min_structs = 0
# sweep over different compositions
for i, nmet2 in enumerate(n):
# INITIALIZE POP object
pop = Pop(nanop.get_atoms_obj_skel().copy(),
nanop.bonds_list,
metals,
shape=shape,
n_metal2=nmet2,
**kwargs)
# run GA simulation
pop.run(max_generations, min_generations, max_nochange)
# if new minimum CE found and <save_data>
# store result in DB
if pop.is_new_min() and save_data:
new_min_structs += 1
tot_new_structs += 1
pop.save_to_db()
outp = 'Completed Size %i of %i (%i new mins)' % (
struct_i + 1, nstructs, new_min_structs)
print('-' * CENTER)
print(outp.center(CENTER))
print('-' * CENTER)
# insert log results into DB
if save_data:
db_inter.insert_bimetallic_log(start_time=start_time,
metal1=metal1,
metal2=metal2,
shape=shape,
ga_generations=max_generations,
shell_range='%i - %i' %
tuple(nshell_range),
new_min_structs=tot_new_structs,
tot_structs=tot_structs,
batch_run_num=batch_runinfo)
from ce_expansion.npdb import db_inter, datatables
"""
Test all results in BimetallicResults to ensure that
the ordering compression algo is working correctly
"""
res = db_inter.get_bimet_result()
for i, r in enumerate(res):
print(f' testing {i:06d} {r.num_atoms:05d} atoms', end='\r')
test = datatables.BimetallicResults(r.metal1, r.metal2, r.shape,
r.num_atoms, r.diameter, r.n_metal1,
r.n_metal2, r.CE, r.EE, r.ordering)
assert (test.ordering == r.ordering).all()
assert test.n_metal2 == r.n_metal2 == sum(map(int, test.ordering))
assert len(r.ordering) == r.num_atoms