pygauss 0.6.0

几何分析

分子可以以静态或交互方式查看。

#mol.show_initial(active=True)
vdw = mol.show_initial(represent='vdw', rotations=[[0,0,90], [-90, 90, 0]])
ball_stick = mol.show_optimisation(represent='ball_stick', rotations=[[0,0,90], [-90, 90, 0]])
display(vdw, ball_stick)

print 'Cl optimised polar coords from aromatic ring : ({0}, {1},{2})'.format(
    *[round(i, 2) for i in mol.calc_polar_coords_from_plane(20,3,2,1)])
ax = mol.plot_opt_trajectory(20, [3,2,1])
ax.set_title('Cl optimisation path')
ax.get_figure().set_size_inches(4, 3)

Cl optimised polar coords from aromatic ring : (0.11, -116.42,-170.06)

能量和频率分析

print('Optimised? {0}, Conformer? {1}, Energy = {2} a.u.'.format(
    mol.is_optimised(), mol.is_conformer(),
    round(mol.get_opt_energy(units='hartree'),3)))
ax = mol.plot_opt_energy(units='hartree')
ax.get_figure().set_size_inches(3, 2)
ax = mol.plot_freq_analysis()
ax.get_figure().set_size_inches(4, 2)

Optimised? True, Conformer? True, Energy = -805.105 a.u.

几何构象的势能扫描分析...

mol2 = pg.molecule.Molecule(folder_obj=folder, alignto=[3,2,1],
            pes_fname=['CJS_emim_6311_plus_d3_scan.log',
                       'CJS_emim_6311_plus_d3_scan_bck.log'])
ax = mol2.plot_pes_scans([1,4,9,10], rotation=[0,0,90], img_pos='local_maxs', zoom=0.5)
ax.set_title('Ethyl chain rotational conformer analysis')
ax.get_figure().set_size_inches(7, 3)

部分电荷分析

使用自然键轨道（NBO）分析

print '+ve charge centre polar coords from aromatic ring: ({0} {1},{2})'.format(
    *[round(i, 2) for i in mol.calc_nbo_charge_center(3, 2, 1)])
display(mol.show_nbo_charges(represent='ball_stick', axis_length=0.4,
                              rotations=[[0,0,90], [-90, 90, 0]]))

+ve charge centre polar coords from aromatic ring: (0.02 -51.77,-33.15)

态密度分析

print 'Number of Orbitals: {}'.format(mol.get_orbital_count())
homo, lumo = mol.get_orbital_homo_lumo()
homoe, lumoe = mol.get_orbital_energies([homo, lumo])
print 'HOMO at {} eV'.format(homoe)
print 'LUMO at {} eV'.format(lumoe)

Number of Orbitals: 272
HOMO at -4.91492036773 eV
LUMO at -1.85989816817 eV

ax = mol.plot_dos(per_energy=1, lbound=-20, ubound=10, legend_size=12)

键合分析

使用二阶微扰理论。

print 'H inter-bond energy = {} kJmol-1'.format(
        mol.calc_hbond_energy(eunits='kJmol-1', atom_groups=['emim', 'cl']))
print 'Other inter-bond energy = {} kJmol-1'.format(
    mol.calc_sopt_energy(eunits='kJmol-1', no_hbonds=True, atom_groups=['emim', 'cl']))
display(mol.show_sopt_bonds(min_energy=1, eunits='kJmol-1',
                            atom_groups=['emim', 'cl'],
                            no_hbonds=True,
                            rotations=[[0, 0, 90]]))
display(mol.show_hbond_analysis(cutoff_energy=5.,alpha=0.6,
                                atom_groups=['emim', 'cl'],
                                rotations=[[0, 0, 90], [90, 0, 0]]))

H inter-bond energy = 111.7128 kJmol-1
Other inter-bond energy = 11.00392 kJmol-1

分子比较

fig, caption = analysis.plot_mol_images(mtype='optimised', max_cols=3,
                        info_columns=['Cation', 'Anion', 'Initial'],
                        rotations=[[0,0,90]])
print caption

(A) emim, cl, B, (B) emim, cl, BE, (C) emim, cl, BM, (D) emim, cl, F, (E) emim, cl, FE

数据比较

fig, caption = analysis.plot_mol_graphs(gtype='dos', max_cols=3,
                                        lbound=-20, ubound=10, legend_size=0,
                                        band_gap_value=False,
                                   info_columns=['Cation', 'Anion', 'Initial'])
print caption

(A) emim, cl, B, (B) emim, cl, BE, (C) emim, cl, BM, (D) emim, cl, F, (E) emim, cl, FE

可以将提到的针对单个分子的方法应用于所有或部分这些计算。

analysis.add_mol_property_subset('Opt', 'is_optimised', rows=[2,3])
analysis.add_mol_property('Energy (au)', 'get_opt_energy', units='hartree')
analysis.add_mol_property('Cation chain, $\\psi$', 'calc_dihedral_angle', [1, 4, 9, 10])
analysis.add_mol_property('Cation Charge', 'calc_nbo_charge', 'emim')
analysis.add_mol_property('Anion Charge', 'calc_nbo_charge', 'cl')
analysis.add_mol_property(['Anion-Cation, $r$', 'Anion-Cation, $\\theta$', 'Anion-Cation, $\\phi$'],
                               'calc_polar_coords_from_plane', 3, 2, 1, 20)
analysis.add_mol_property('Anion-Cation h-bond', 'calc_hbond_energy',
                          eunits='kJmol-1', atom_groups=['emim', 'cl'])
analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],
                   column_index=['Cation', 'Anion', 'Anion-Cation'])

还有选项（需要 pdflatex 和 ghostscript+imagemagik）将表格以 LaTeX 格式图像输出。

analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],
                   column_index=['Cation', 'Anion', 'Anion-Cation'],
                   as_image=True, font_size=12)

多变量分析

RadViz 是一种可视化多变量数据的方法。

ax = analysis.plot_radviz_comparison('Anion', columns=range(4, 10))

KMeans 算法通过尝试将样本分成具有相同方差的 n 组来聚类数据。

pg.utils.imgplot_kmean_groups(
    analysis, 'Anion', 'cl', 4, range(4, 10),
    output=['Initial'], mtype='optimised',
    rotations=[[0, 0, 90], [-90, 90, 0]],
    axis_length=0.3)

(A) BM

(A) FE

(A) B, (B) BE

(A) F