def plot_irc(axes, i, d, f):
#print(f)
Eact, xyz, spc, Rc = pyg.read_irc(d + f)
Eact = hdt.hatokcal * Eact
xyz = xyz[1:]
Eact = Eact[1:]
Rc = Rc[:-1]
#print(Rc[:,1])
#print(Eact-Eact.min() - Rc[:,1]-Rc[:,1].min())
s_idx = f.split('IRC')[1].split('.')[0]
hdt.writexyzfile(c + f.split('.')[0] + '.xyz', xyz, spc)
#print(f.split('IRC')[1].split('.')[0],Rc.shape)
if Rc.size > 10:
#------------ CV NETWORKS 1 -----------
energies1 = []
N = 0
for comp in nc1:
comp.setConformers(confs=xyz, types=list(spc))
energies1.append(hdt.hatokcal * comp.energy())
N = N + 1
energies2 = []
N = 0
for comp in nc2:
comp.setConformers(confs=xyz, types=list(spc))
energies2.append(hdt.hatokcal * comp.energy())
N = N + 1
modl_std1 = np.std(energies1, axis=0)[::-1]
energies1 = np.mean(np.vstack(energies1), axis=0)
modl_std2 = np.std(energies2, axis=0)[::-1]
energies2 = np.mean(np.vstack(energies2), axis=0)
rmse1 = hdt.calculaterootmeansqrerror(energies1, Eact)
rmse2 = hdt.calculaterootmeansqrerror(energies2, Eact)
dba = Eact.max() - Eact[0]
db1 = energies1.max() - energies1[0]
db2 = energies2.max() - energies2[0]
rpa = Eact[0] - Eact[-1]
rp1 = energies1[0] - energies1[-1]
rp2 = energies2[0] - energies2[-1]
bar1.append(abs(db1 - dba))
bar2.append(abs(db2 - dba))
rmp1.append(abs(rpa - rp1))
rmp2.append(abs(rpa - rp2))
Ec1.append(energies1)
Ec2.append(energies2)
Ea.append(Eact)
print(i, ')', f, ':', len(spc), ':', rmse1, rmse2, 'R/P1: ',
abs(rpa - rp1), 'R/P2: ', abs(rpa - rp2), 'Barrier1:',
abs(db1 - dba), 'Barrier2:', abs(db2 - dba))
Rce = hdt.hatokcal * (Rc[:, 0] - Rc[:, 0][0])
Rce1 = energies2[::-1] - energies2[::-1][0]
axes.set_xlim([Rc.min(), Rc.max()])
axes.set_ylim([Rce.min() - 1.0, Rce1.max() + 20.0])
axes.plot(Rc[:, 1],
hdt.hatokcal * (Rc[:, 0] - Rc[:, 0][0]),
color='Black',
label='DFT')
axes.errorbar(Rc[:, 1],
energies2[::-1] - energies2[::-1][0],
yerr=modl_std2,
fmt='--',
color='red',
label="ANI-1: " + "{:.1f}".format(bar2[-1]),
linewidth=2)
axes.errorbar(Rc[:, 1],
energies1[::-1] - energies1[::-1][0],
yerr=modl_std1,
fmt='--',
color='blue',
label="[" + str(i) + "]: " + "{:.1f}".format(bar1[-1]),
linewidth=2)
#axes.set_xlabel("Reaction Coordinate $\AA$")
#axes.set_ylabel(r"$\Delta E$ $ (kcal \times mol^{-1})$")
#axes.plot(Rc[:, 1], energies2[::-1]-energies2[::-1][0],'--',color='red',label="["+str(i)+"]: "+"{:.1f}".format(bar2[-1]),linewidth=3)
#axes.plot(Rc[:, 1], energies1[::-1]-energies1[::-1][0],'--',color='green',label="["+str(i)+"]: "+"{:.1f}".format(bar1[-1]),linewidth=3)
axes.legend(loc="upper left", fontsize=10)
axes.set_title(str(f),
color='black',
fontdict={'weight': 'bold'},
x=0.8,
y=0.85)
import pygau09tools as g09 import hdnntools as hdn import numpy as np import matplotlib.pyplot as plt import pyNeuroChem as pync #dir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/scans_double_bond_migration/' #dir = '/home/jujuman/Dropbox/IRC_DBondMig/rxn1/' dir = '/home/jujuman/Dropbox/IRC_DBondMig/rxn5/' filef = dir + 'IRC_fwd.log' fileb = dir + 'IRC_bck.log' dataf = g09.read_irc(filef) datab = g09.read_irc(fileb) xyz = np.concatenate([np.flipud(datab[1]), dataf[1]]) eng = hdn.hatokcal * np.concatenate([np.flipud(datab[0]), dataf[0]]) print(xyz.shape) hdn.writexyzfile(dir + 'scan.xyz', xyz, dataf[2]) # Set required files for pyNeuroChem # Set required files for pyNeuroChem anipath = '/home/jujuman/Dropbox/ChemSciencePaper.AER/ANI-c08e-ntwk' cnstfile = anipath + '/rHCNO-4.6A_16-3.1A_a4-8.params' saefile = anipath + '/sae_6-31gd.dat' nnfdir = anipath + '/networks/'
def plot_irc_data(axes, file, title, ntwl, cnstfile, saefile, dir, trained):
Eact, xyz, typ, Rc = pyg.read_irc(file)
Rc = Rc[:, 1]
Rc = Rc[::-1]
print(Eact.shape, Rc.shape, xyz.shape)
# Shift reference to reactant
#Eact = Eact[::-1]
Eact = hdt.hatokcal * (Eact - Eact[-1])
# Plot reference results
axes.scatter(Rc, Eact, color='black', linewidth=3)
# Plot ANI results
color = cm.rainbow(np.linspace(0, 1, len(ntwl)))
terr = np.zeros(len(ntwl))
derr = np.zeros(len(ntwl))
berr = np.zeros(len(ntwl))
for i, (nt, c) in enumerate(zip(ntwl, color)):
ncr = pync.conformers(dir + nt[0] + cnstfile, dir + nt[0] + saefile,
rcdir + nt[0] + 'networks/', 0, True)
# Set the conformers in NeuroChem
ncr.setConformers(confs=xyz, types=list(typ))
# Compute Energies of Conformations
E1 = ncr.energy()
# Shift ANI E to reactant
E1 = hdt.hatokcal * (E1 - E1[-1])
# Calculate error
errn = hdt.calculaterootmeansqrerror(E1, Eact)
terr[i] = errn
derr[i] = np.abs(
np.abs((E1[0] - E1[-1])) - np.abs((Eact[0] - Eact[-1])))
berr[i] = np.abs(E1.max() - Eact.max())
# Plot
axes.plot(Rc,
E1,
'r--',
color=c,
label="[" + str(i) + "]: " + "{:.1f}".format(berr[i]),
linewidth=2)
#axes.set_xlim([Rc.min(), Rc.max()])
#axes.set_ylim([-15, 70])
axes.legend(loc="upper left", fontsize=7)
if trained:
axes.set_title(title,
color='green',
fontdict={'weight': 'bold'},
x=0.83,
y=0.70)
else:
axes.set_title(title,
color='red',
fontdict={'weight': 'bold'},
x=0.83,
y=0.70)
return terr, derr, berr
#cnstfile = 'rHCNO-4.6A_16-3.1A_a4-8.params'
#saefile = 'sae_6-31gd.dat'
Nnc = 2
files = os.listdir(d)
files.sort()
#print(files)
# Construct pyNeuroChem classes
nc1 = [pync.conformers(wkdir1 + cnstfile, wkdir1 + saefile, wkdir1 + 'train' + str(l) + '/networks/', 0, False) for l in range(Nnc)]
comp_xyz = []
for f in files:
#print(f)
Eact, xyz, spc, Rc = pyg.read_irc(d+f)
s_idx = f.split('IRC')[1].split('.')[0]
hdt.writexyzfile(c+f.split('.')[0]+'.xyz',xyz,spc)
#print(f.split('IRC')[1].split('.')[0],Rc.shape)
if Rc.size > 10:
#------------ CV NETWORKS 1 -----------
energies = []
N = 0
for comp in nc1:
comp.setConformers(confs=xyz, types=list(spc))
energies.append(hdt.hatokcal*comp.energy())
N = N + 1
energies = np.vstack(energies)
modl_std = np.std(energies[::-1],axis=0) / float(len(spc))
import numpy as np
import pygau09tools as pyg
import hdnntools as hdt
n = '10'
fpf = 'benz_rxn_' + str(n)
wkdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/benz_dbm_rxns_full/irc_dbm_benz_'+str(n)+'/'
#dtdir = '/home/jujuman/Dropbox/IRC_DBondMig/Benzene_rxn/rxn_ben'+str(n)+'/'
dtdir = '/home/jujuman/Dropbox/IRC_DBondMig/Benzene_rxn2/rxn_ben'+str(n)+'/IRC.log'
#en, cd, ty = pyg.get_irc_data(dtdir+'IRC_fwd.log',dtdir+'IRC_bck.log',dtdir+'saddle_ts.log')
en, cd, ty, Rc = pyg.read_irc(dtdir)
np.savez(wkdir+'reaction_coordinate.npz',x=Rc)
Na = len(ty)
Nc = en.shape[0]
print (cd.shape,' ',en.shape)
#for i,x in enumerate(cd):
#hdt.write_rcdb_input(x,ty,i,wkdir,fpf,5,'wb97x/6-31g*','600.0',opt='0')
hdt.writexyzfile(wkdir+'irc.xyz',cd,ty)
f = open(wkdir+'irc.dat','w')
f.write("comment\n")
f.write(str(Nc)+'\n')
f.write(str(Na)+',')
for j in ty: f.write(j+',')
import pygau09tools as g09
import hdnntools as hdn
import numpy as np
wkdir = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/scans_double_bond_migration_1/'
filef = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/scans_double_bond_migration_1/IRC_fwd.log'
#fileb = '/home/jujuman/Research/GDB-11-wB97X-6-31gd/dnnts_rxns/scans_double_bond_migration/IRC_bck.log'
dataf = g09.read_irc(filef)
#datab = g09.read_irc(fileb)
#xyz = np.concatenate([np.flipud(datab[1]),dataf[1]])
xyz = dataf[1]
for i,x in enumerate(xyz):
hdn.write_rcdb_input(x,dataf[2],i,wkdir,'double_B_mig',50,'wb97x/6-31g*','1000.0',opt='0')