V_zc = (V_cathode + V_anode) / 2
V_Q_electrode[V_zc] = 0
else:
q_cathode = df.rho_q[
args.cathode] * dz / NANO**2 * ELEMENTARY_CHARGE / MILLI # mC/m^2
q_anode = df.rho_q[
args.anode] * dz / NANO**2 * ELEMENTARY_CHARGE / MILLI # mC/m^2
if abs(q_cathode + q_anode) > 1E6:
print('WARNING: charges on cathode and anode do not equal: %s' %
inp)
V_Q_electrode[V_cathode] = q_cathode
V_Q_electrode[V_anode] = q_anode
V_Q_cell[V_drop] = q_cathode
V_list, Q_list = zip(*sorted(V_Q_electrode.items()))
coeff4, score4 = polyfit(V_list, Q_list, 4)
coeff5, score5 = polyfit(V_list, Q_list, 5)
print('Zero charge voltage: %.4f V' % V_zc)
print('RSQ for 4th and 5th polynomial fitting Cdiff: %.4f %.4f' %
(score4, score5))
print('%10s %10s %10s %10s %10s %10s' %
('electrode', 'V', 'Q', 'Cint', 'Cdiff-4th', 'Cdiff-5th'))
for V, Q in sorted(V_Q_electrode.items()):
Cdiff4 = polyval_derivative(V, coeff4)[1]
Cdiff5 = polyval_derivative(V, coeff5)[1]
Cint = Q / (V - V_zc) if V != V_zc else 0
print('%10s %10.4f %10.4f %10.4f %10.4f %10.4f' %
('', V, Q, Cint, Cdiff4, Cdiff5))
print('%10s %10s %10s %10s' % ('cell', 'V', 'Q', 'Cint'))
for V, Q in sorted(V_Q_cell.items()):
def post_process(T_list, P_list, result_list, n_mol_list,
**kwargs) -> (dict, str):
t_set = set(T_list)
p_set = set(P_list)
if len(t_set) < 5 or len(p_set) < 5:
return None, 'T or P points less than 5'
from mstools.analyzer.fitting import polyfit_2d, polyfit
def round3(x):
return float('%.3e' % x)
### einter divided by number of molecules
dens_stderr_list = [
list(map(round3, result['density'])) for result in result_list
]
eint_stderr_list = [
list(map(lambda x: round3(x / n_mol_list[0]), result['einter']))
for result in result_list
]
comp_stderr_list = [
list(map(round3, result['compress'])) for result in result_list
]
dens_list = [i[0] for i in dens_stderr_list]
eint_list = [i[0] for i in eint_stderr_list]
comp_list = [i[0] for i in comp_stderr_list]
### Fit against T, P with poly4
coeff_dens, score_dens = polyfit_2d(T_list, P_list, dens_list, 4)
coeff_eint, score_eint = polyfit_2d(T_list, P_list, eint_list, 4)
### Fit against only T with poly3
t_p_dens_list = list(map(list, zip(T_list, P_list, dens_list)))
t_p_dens_list.sort(key=lambda x: (x[1], x[0])) # sorted by P, then T
t_p_eint_list = list(map(list, zip(T_list, P_list, eint_list)))
t_p_eint_list.sort(key=lambda x: (x[1], x[0])) # sorted by P, then T
t_p_comp_list = list(map(list, zip(T_list, P_list, comp_list)))
t_p_comp_list.sort(key=lambda x: (x[1], x[0])) # sorted by P, then T
t_p_dens_stderr_list = list(
map(list, zip(T_list, P_list, dens_stderr_list)))
t_p_dens_stderr_list.sort(key=lambda x:
(x[1], x[0])) # sorted by P, then T
t_p_eint_stderr_list = list(
map(list, zip(T_list, P_list, eint_stderr_list)))
t_p_eint_stderr_list.sort(key=lambda x:
(x[1], x[0])) # sorted by P, then T
t_p_comp_stderr_list = list(
map(list, zip(T_list, P_list, comp_stderr_list)))
t_p_comp_stderr_list.sort(key=lambda x:
(x[1], x[0])) # sorted by P, then T
t_dens_poly3 = {}
t_eint_poly3 = {}
t_comp_poly3 = {}
for p in sorted(p_set):
_t_list = [
element[0] for element in t_p_dens_list if element[1] == p
]
_dens_list = [
element[2] for element in t_p_dens_list if element[1] == p
]
_eint_list = [
element[2] for element in t_p_eint_list if element[1] == p
]
_comp_list = [
element[2] for element in t_p_comp_list if element[1] == p
]
if len(_t_list) < 5:
continue
# density-T relation is fitted by 3-order polynomial function
_t_dens_coeff, _t_dens_score = polyfit(_t_list, _dens_list, 3)
_t_eint_coeff, _t_eint_score = polyfit(_t_list, _eint_list, 3)
_t_comp_coeff, _t_comp_score = polyfit(_t_list, _comp_list, 3)
t_dens_poly3[p] = [
list(map(round3, _t_dens_coeff)),
round3(_t_dens_score),
min(_t_list),
max(_t_list)
]
t_eint_poly3[p] = [
list(map(round3, _t_eint_coeff)),
round3(_t_eint_score),
min(_t_list),
max(_t_list)
]
t_comp_poly3[p] = [
list(map(round3, _t_comp_coeff)),
round3(_t_comp_score),
min(_t_list),
max(_t_list)
]
post_result = {
'density': t_p_dens_stderr_list,
'einter': t_p_eint_stderr_list,
'compress': t_p_comp_stderr_list,
'density-poly4':
[list(map(round3, coeff_dens)),
round3(score_dens)],
'einter-poly4':
[list(map(round3, coeff_eint)),
round3(score_eint)],
'density-t-poly3': t_dens_poly3,
'einter-t-poly3': t_eint_poly3,
'compress-t-poly3': t_comp_poly3,
}
return post_result, 'density-poly4-score %.4f einter-poly4-score %.4f' % (
score_dens, score_eint)
def get_post_data(post_result, T, P, smiles_list, **kwargs) -> dict:
from mstools.analyzer.fitting import polyval_derivative_2d, polyval, polyval_derivative, polyfit
### Calculate with T,P-poly4. Not accurate enough, especially for expansion and compressibility
coeff_dens, score_dens = post_result['density-poly4']
coeff_eint, score_eint = post_result['einter-poly4']
density4, dDdT4, dDdP4 = polyval_derivative_2d(T, P, 4,
coeff_dens) # g/mL
einter4, dEdT4, dEdP4 = polyval_derivative_2d(T, P, 4,
coeff_eint) # kJ/mol
expansion4 = -1 / density4 * dDdT4 # K^-1
compressibility4 = 1 / density4 * dDdP4 # bar^-1
import pybel
py_mol = pybel.readstring('smi', smiles_list[0])
cp_inter4 = dEdT4 * 1000 # J/mol.K
cp_pv4 = -py_mol.molwt * P / density4**2 * dDdT4 * 0.1 # J/mol/K
### T-poly3
_p_dens_list = []
_p_eint_list = []
_p_comp_list = []
_p_dDdT_list = []
_p_dEdT_list = []
for _p in post_result['density-t-poly3']:
coef, score, tmin, tmax = post_result['density-t-poly3'][str(_p)]
if score < 0.999 or T < tmin - 10 or T > tmax + 10:
continue
dens, dDdT = polyval_derivative(T, coef)
_p_dens_list.append([float(_p), dens])
_p_dDdT_list.append([float(_p), dDdT])
for _p in post_result['einter-t-poly3']:
coef, score, tmin, tmax = post_result['einter-t-poly3'][str(_p)]
if score < 0.999 or T < tmin - 10 or T > tmax + 10:
continue
eint, dEdT = polyval_derivative(T, coef)
_p_eint_list.append([float(_p), eint])
_p_dEdT_list.append([float(_p), dEdT])
for _p in post_result['compress-t-poly3']:
coef, score, tmin, tmax = post_result['compress-t-poly3'][str(_p)]
if score < 0.95 or T < tmin - 10 or T > tmax + 10:
continue
_p_comp_list.append([float(_p), polyval(T, coef)])
### Default value
density = None
einter = None
hvap = None
cp_inter = None
cp_pv = None
expansion = None
compressibility = None
if len(_p_dens_list) >= 5:
coef, score = polyfit(*zip(*_p_dens_list), 3)
_p_list = list(zip(*_p_dens_list))[0]
if P > min(_p_list) - 10 and P < max(_p_list) + 10:
density = polyval(P, coef)
coef, score = polyfit(*zip(*_p_dDdT_list), 3)
dDdT = polyval(P, coef)
expansion = -1 / density * dDdT # K^-1
cp_pv = -py_mol.molwt * P / density**2 * dDdT * 0.1 # J/mol/K
if len(_p_eint_list) >= 5:
coef, score = polyfit(*zip(*_p_eint_list), 3)
_p_list = list(zip(*_p_eint_list))[0]
if P > min(_p_list) - 10 and P < max(_p_list) + 10:
einter = polyval(P, coef)
hvap = 8.314 * T / 1000 - einter # kJ/mol
coef, score = polyfit(*zip(*_p_dEdT_list), 3)
dEdT = polyval(P, coef)
cp_inter = dEdT * 1000 # J/mol.K
if len(_p_comp_list) >= 5:
coef, score = polyfit(*zip(*_p_comp_list), 3)
_p_list = list(zip(*_p_comp_list))[0]
if P > min(_p_list) - 10 and P < max(_p_list) + 10:
compressibility = polyval(P, coef)
return {
'density': density,
'einter': einter,
'hvap': hvap,
'cp_inter': cp_inter,
'cp_pv': cp_pv,
'expansion': expansion,
'compress': compressibility,
'density-poly4-score': score_dens,
'einter-poly4-score': score_eint,
'density-poly4': density4,
'einter-poly4': einter4,
'cp_inter-poly4': cp_inter4,
'cp_pv-poly4': cp_pv4,
'expansion-poly4': expansion4,
'compress-poly4': compressibility4,
}
for mol in mols:
try:
os.chdir(os.path.join(Config.WORK_DIR, 'Cv', mol.name))
except:
print(mol, 'Error: Dir not exist', file=fout)
continue
gauss.logs = ['conf-%i.log' % i for i in range(n_conformer)]
try:
result = gauss.analyze()
except Exception as e:
print(mol, str(e), file=fout)
continue
T_list = []
Cv_list = []
for T, val_stderr in result['Cv-corrected'].items():
T_list.append(T)
Cv_list.append(val_stderr[0])
if T_list != [100, 200, 300, 400, 500, 600, 700]:
print(mol, 'Some temperatures are failed', file=fout)
else:
coef, score = polyfit(T_list, Cv_list, 4)
print(mol, *coef, score, file=fout)
os.chdir(CWD)
fout.close()
if cmd == 'save-db':
Cv.load_from_log('_cv.log')
