本文整理汇总了Python中gpaw.xc.XC.get_energy_and_potential方法的典型用法代码示例。如果您正苦于以下问题：Python XC.get_energy_and_potential方法的具体用法？Python XC.get_energy_and_potential怎么用？Python XC.get_energy_and_potential使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类gpaw.xc.XC的用法示例。
在下文中一共展示了XC.get_energy_and_potential方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: C_XC
# 需要导入模块: from gpaw.xc import XC [as 别名]
# 或者: from gpaw.xc.XC import get_energy_and_potential [as 别名]
class C_XC(Contribution):
def __init__(self, nlfunc, weight, functional = 'LDA'):
Contribution.__init__(self, nlfunc, weight)
self.functional = functional
def get_name(self):
return 'XC'
def get_desc(self):
return "("+self.functional+")"
def initialize(self):
self.xc = XC(self.functional)
self.vt_sg = self.nlfunc.finegd.empty(self.nlfunc.nspins)
self.e_g = self.nlfunc.finegd.empty()
def initialize_1d(self):
self.ae = self.nlfunc.ae
self.xc = XC(self.functional)
self.v_g = np.zeros(self.ae.N)
def calculate_spinpaired(self, e_g, n_g, v_g):
self.e_g[:] = 0.0
self.vt_sg[:] = 0.0
self.xc.calculate(self.nlfunc.finegd, n_g[None, ...], self.vt_sg,
self.e_g)
v_g += self.weight * self.vt_sg[0]
e_g += self.weight * self.e_g
def calculate_spinpolarized(self, e_g, na_g, va_g, nb_g, vb_g):
self.e_g[:] = 0.0
self.vt_sg[:] = 0.0
self.xc.get_energy_and_potential(na_g, self.vt_sg[0], nb_g, self.vt_sg[1], e_g=self.e_g)
va_g += self.weight * self.vt_sg[0]
vb_g += self.weight * self.vt_sg[1]
e_g += (self.weight * self.e_g).ravel()
def calculate_energy_and_derivatives(self, D_sp, H_sp, a):
# Get the XC-correction instance
c = self.nlfunc.setups[a].xc_correction
assert self.nlfunc.nspins == 1
D_p = D_sp[0]
dEdD_p = H_sp[0][:]
D_Lq = dot3(c.B_pqL.T, D_p)
n_Lg = np.dot(D_Lq, c.n_qg)
n_Lg[0] += c.nc_g * sqrt(4 * pi)
nt_Lg = np.dot(D_Lq, c.nt_qg)
nt_Lg[0] += c.nct_g * sqrt(4 * pi)
dndr_Lg = np.zeros((c.Lmax, c.ng))
dntdr_Lg = np.zeros((c.Lmax, c.ng))
for L in range(c.Lmax):
c.rgd.derivative(n_Lg[L], dndr_Lg[L])
c.rgd.derivative(nt_Lg[L], dntdr_Lg[L])
E = 0
vt_g = np.zeros(c.ng)
v_g = np.zeros(c.ng)
e_g = np.zeros(c.ng)
y = 0
for w, Y_L in zip(weight_n, c.Y_nL):
A_Li = rnablaY_nLv[y, :c.Lmax]
a1x_g = np.dot(A_Li[:, 0], n_Lg)
a1y_g = np.dot(A_Li[:, 1], n_Lg)
a1z_g = np.dot(A_Li[:, 2], n_Lg)
a2_g = a1x_g**2 + a1y_g**2 + a1z_g**2
a2_g[1:] /= c.rgd.r_g[1:]**2
a2_g[0] = a2_g[1]
a1_g = np.dot(Y_L, dndr_Lg)
a2_g += a1_g**2
deda2_g = np.zeros(c.ng)
v_g[:] = 0.0
e_g[:] = 0.0
n_g = np.dot(Y_L, n_Lg)
self.xc.kernel.calculate(e_g, n_g.reshape((1, -1)),
v_g.reshape((1, -1)),
a2_g.reshape((1, -1)),
deda2_g.reshape((1, -1)))
E += w * np.dot(e_g, c.rgd.dv_g)
x_g = -2.0 * deda2_g * c.rgd.dv_g * a1_g
c.rgd.derivative2(x_g, x_g)
x_g += v_g * c.rgd.dv_g
dEdD_p += self.weight * w * np.dot(dot3(c.B_pqL, Y_L),
np.dot(c.n_qg, x_g))
x_g = 8.0 * pi * deda2_g * c.rgd.dr_g
dEdD_p += w * np.dot(dot3(c.B_pqL,
A_Li[:, 0]),
np.dot(c.n_qg, x_g * a1x_g))
dEdD_p += w * np.dot(dot3(c.B_pqL,
A_Li[:, 1]),
np.dot(c.n_qg, x_g * a1y_g))
dEdD_p += w * np.dot(dot3(c.B_pqL,
A_Li[:, 2]),
np.dot(c.n_qg, x_g * a1z_g))
n_g = np.dot(Y_L, nt_Lg)
a1x_g = np.dot(A_Li[:, 0], nt_Lg)
a1y_g = np.dot(A_Li[:, 1], nt_Lg)
#.........这里部分代码省略.........