Пример #1
0
 def dot_dm_ket(ket_re, ket_im):
     # DM * ket: e.g. ir denotes dm_im | ao_re >
     c0_rr = _dot_ao_dm(cell, ket_re, dm_re, nao, ngrids, non0tab)
     c0_ri = _dot_ao_dm(cell, ket_im, dm_re, nao, ngrids, non0tab)
     c0_ir = _dot_ao_dm(cell, ket_re, dm_im, nao, ngrids, non0tab)
     c0_ii = _dot_ao_dm(cell, ket_im, dm_im, nao, ngrids, non0tab)
     return c0_ri, c0_rr, c0_ir, c0_ii
Пример #2
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 def dot_dm_ket(ket_re, ket_im):
     # DM * ket: e.g. ir denotes dm_im | ao_re >
     c0_rr = _dot_ao_dm(cell, ket_re, dm_re, nao, ngrids, non0tab)
     c0_ri = _dot_ao_dm(cell, ket_im, dm_re, nao, ngrids, non0tab)
     c0_ir = _dot_ao_dm(cell, ket_re, dm_im, nao, ngrids, non0tab)
     c0_ii = _dot_ao_dm(cell, ket_im, dm_im, nao, ngrids, non0tab)
     return c0_ri, c0_rr, c0_ir, c0_ii
Пример #3
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def _dm2c_to_rho2x2(mol, ao, dm, non0tab, shls_slice, ao_loc, out=None):
    aoa, aob = ao
    out = _dot_ao_dm(mol, aoa, dm, non0tab, shls_slice, ao_loc, out=out)
    rhoaa = numpy.einsum('pi,pi->p', aoa.real, out.real)
    rhoaa+= numpy.einsum('pi,pi->p', aoa.imag, out.imag)
    rhoba = numpy.einsum('pi,pi->p', aob, out.conj())
    out = _dot_ao_dm(mol, aob, dm, non0tab, shls_slice, ao_loc, out=out)
    rhoab = numpy.einsum('pi,pi->p', aoa, out.conj())
    rhobb = numpy.einsum('pi,pi->p', aob.real, out.real)
    rhobb+= numpy.einsum('pi,pi->p', aob.imag, out.imag)
    return rhoaa, rhoab, rhoba, rhobb
Пример #4
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def _dm2c_to_rho2x2(mol, ao, dm, non0tab, shls_slice, ao_loc, out=None):
    aoa, aob = ao
    out = _dot_ao_dm(mol, aoa, dm, non0tab, shls_slice, ao_loc, out=out)
    rhoaa = numpy.einsum('pi,pi->p', aoa.real, out.real)
    rhoaa += numpy.einsum('pi,pi->p', aoa.imag, out.imag)
    rhoba = numpy.einsum('pi,pi->p', aob, out.conj())
    out = _dot_ao_dm(mol, aob, dm, non0tab, shls_slice, ao_loc, out=out)
    rhoab = numpy.einsum('pi,pi->p', aoa, out.conj())
    rhobb = numpy.einsum('pi,pi->p', aob.real, out.real)
    rhobb += numpy.einsum('pi,pi->p', aob.imag, out.imag)
    return rhoaa, rhoab, rhoba, rhobb
Пример #5
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def _grid_ao2mo(mol, ao, mo_coeff, non0tab=None, shls_slice=None, ao_loc=None):
    ''' ao[deriv,grid,AO].mo_coeff[AO,MO]->mo[deriv,grid,MO]
    ASSUMES that ao is in data layout (deriv,AO,grid) in row-major order!
    mo is returned in data layout (deriv,MO,grid) in row-major order '''
    nderiv, ngrid, nao = ao.shape
    nmo = mo_coeff.shape[-1]
    mo = np.empty((nderiv, nmo, ngrid), dtype=mo_coeff.dtype,
                  order='C').transpose(0, 2, 1)
    if shls_slice is None: shls_slice = (0, mol.nbas)
    if ao_loc is None: ao_loc = mol.ao_loc_nr()
    for ideriv in range(nderiv):
        ao_i = ao[ideriv, :, :]
        mo[ideriv] = _dot_ao_dm(mol,
                                ao_i,
                                mo_coeff,
                                non0tab,
                                shls_slice,
                                ao_loc,
                                out=mo[ideriv])
    return mo
Пример #6
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def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff[0].shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)

    vmata = numpy.zeros((mol.natm,3,3,nao,nao))
    vmatb = numpy.zeros((mol.natm,3,3,nao,nao))
    ipipa = numpy.zeros((3,3,nao,nao))
    ipipb = numpy.zeros((3,3,nao,nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]
            vrho = vxc[0]
            u_u, u_d, d_d = fxc[0].T

            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight*vrho[:,0])
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight*vrho[:,1])
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
            ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1]) * 2
                rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1]) * 2

                wv = u_u * rho1a + u_d * rho1b
                wv *= weight
                # aow ~ rho1 ~ d/dR1
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)

                wv = u_d * rho1a + d_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_gga_wv0((rhoa,rhob), vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wva)
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = rks_grad._make_dR_dao_w(ao, wvb)
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                       for i in range(4)]
            ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                       for i in range(4)]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices)
                wva[0], wvb[0] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[0],dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[1],dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[2],dR_rho1b[2]), vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wva[0])
                rks_grad._d1_dot_(vmata[ia,0], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wva[1])
                rks_grad._d1_dot_(vmata[ia,1], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wva[2])
                rks_grad._d1_dot_(vmata[ia,2], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wva)
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)

                aow = rks_grad._make_dR_dao_w(ao, wvb[0])
                rks_grad._d1_dot_(vmatb[ia,0], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[1])
                rks_grad._d1_dot_(vmatb[ia,1], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[2])
                rks_grad._d1_dot_(vmatb[ia,2], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wvb)
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,p0:p1].transpose(1,0,3,2)
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,p0:p1].transpose(1,0,3,2)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmata, vmatb
Пример #7
0
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    vmat = numpy.zeros((mol.natm, 3, nao, nao))
    max_memory = max(2000, max_memory - vmat.size * 8 / 1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, 'LDA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice,
                                       ao_loc)
            aow1 = numpy.einsum('xpi,p->xpi', ao[1:], weight * vrho)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # First order density = rho1 * 2.  *2 is not applied because + c.c. in the end
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                    ao_dm0[:, p0:p1])
                aow = numpy.einsum('pi,xp->xpi', ao[0], weight * frr * rho1)
                aow[:, :, p0:p1] += aow1[:, :, p0:p1]
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc,
                                  True)
            ao_dm0 = aow = aow1 = None

        for ia in range(mol.natm):
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0, 2, 1)

    elif xctype == 'GGA':
        ao_deriv = 2
        v_ip = numpy.zeros((3, nao, nao))
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, 'GGA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            rks_grad._gga_grad_sum_(v_ip, mol, ao, wv, mask, ao_loc)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wv)
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc,
                                  True)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, p0:p1] += v_ip[:, p0:p1]
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0, 2, 1)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmat
Пример #8
0
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    vmat = numpy.zeros((mol.natm, 3, 3, nao, nao))
    ipip = numpy.zeros((3, 3, nao, nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, 'LDA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight * vrho)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice,
                                       ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                    ao_dm0[:, p0:p1]) * 2
                # aow ~ rho1 ~ d/dR1
                aow = numpy.einsum('pi,xp->xpi', ao[0], weight * frr * rho1)
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, :, p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, 'GGA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wv)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wv[0])
                rks_grad._d1_dot_(vmat[ia, 0], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wv[1])
                rks_grad._d1_dot_(vmat[ia, 1], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wv[2])
                rks_grad._d1_dot_(vmat[ia, 2], mol, aow, ao[0], mask, ao_loc,
                                  True)

                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wv)
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, :, p0:p1]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, p0:p1].transpose(1, 0, 3, 2)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmat
Пример #9
0
def _dot_spinor_dm(mol, ket, dm, non0tab, shls_slice, ao_loc):
    ket_a, ket_b = ket
    outa = _dot_ao_dm(mol, ket_a, dm, non0tab, shls_slice, ao_loc)
    outb = _dot_ao_dm(mol, ket_b, dm, non0tab, shls_slice, ao_loc)
    return outa, outb
Пример #10
0
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff[0].shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)

    vmata = numpy.zeros((mol.natm,3,nao,nao))
    vmatb = numpy.zeros((mol.natm,3,nao,nao))
    max_memory = max(2000, max_memory-(vmata.size+vmatb.size)*8/1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]
            vrho = vxc[0]
            u_u, u_d, d_d = fxc[0].T

            ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
            ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
            aow1a = numpy.einsum('xpi,p->xpi', ao[1:], weight*vrho[:,0])
            aow1b = numpy.einsum('xpi,p->xpi', ao[1:], weight*vrho[:,1])
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
# First order density = rho1 * 2.  *2 is not applied because + c.c. in the end
                rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1])
                rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1])

                wv = u_u * rho1a + u_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                aow[:,:,p0:p1] += aow1a[:,:,p0:p1]
                rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)

                wv = u_d * rho1a + d_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                aow[:,:,p0:p1] += aow1b[:,:,p0:p1]
                rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0a = ao_dm0b = aow = aow1a = aow1b = None

        for ia in range(mol.natm):
            vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
            vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)

    elif xctype == 'GGA':
        ao_deriv = 2
        vipa = numpy.zeros((3,nao,nao))
        vipb = numpy.zeros((3,nao,nao))
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_gga_wv0((rhoa,rhob), vxc, weight)
            rks_grad._gga_grad_sum_(vipa, mol, ao, wva, mask, ao_loc)
            rks_grad._gga_grad_sum_(vipb, mol, ao, wvb, mask, ao_loc)

            ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                       for i in range(4)]
            ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                       for i in range(4)]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices)
                wva[0], wvb[0] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[0],dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[1],dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[2],dR_rho1b[2]), vxc, fxc, weight)

                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wva)
                rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wvb)
                rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,p0:p1] += vipa[:,p0:p1]
            vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
            vmatb[ia,:,p0:p1] += vipb[:,p0:p1]
            vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmata, vmatb
Пример #11
0
def eval_rho(mol, ao, dm, non0tab=None, xctype='LDA', verbose=None):
    '''Collocate the *real* density (opt. gradients) on the real-space grid.

    Args:
        mol : instance of :class:`Mole` or :class:`Cell`

        ao : ([4,] nx*ny*nz, nao=cell.nao_nr()) ndarray
            The value of the AO crystal orbitals on the real-space grid by default.
            If xctype='GGA', also contains the value of the gradient in the x, y,
            and z directions.

    Returns:
        rho : ([4,] nx*ny*nz) ndarray
            The value of the density on the real-space grid. If xctype='GGA',
            also contains the value of the gradient in the x, y, and z
            directions.

    See Also:
        pyscf.dft.numint.eval_rho

    '''

    assert(ao.flags.c_contiguous)
    if xctype == 'GGA':
        ngrids, nao = ao[0].shape
    else:
        ngrids, nao = ao.shape

    if non0tab is None:
        non0tab = numpy.ones(((ngrids+BLKSIZE-1)//BLKSIZE,mol.nbas),
                             dtype=numpy.int8)

    # if xctype == 'GGA':
    #     rho = numpy.empty((4,ngrids))
    #     c0 = _dot_ao_dm(mol, ao[0], dm, nao, ngrids, non0tab)
    #     rho[0] = numpy.einsum('pi,pi->p', ao[0], c0)
    #     for i in range(1, 4):
    #         c1 = _dot_ao_dm(mol, ao[i], dm, nao, ngrids, non0tab)
    #         rho[i] = numpy.einsum('pi,pi->p', ao[0], c1) * 2 # *2 for +c.c.
    # else:
    #     c0 = _dot_ao_dm(mol, ao, dm, nao, ngrids, non0tab)
    #     rho = numpy.einsum('pi,pi->p', ao, c0)
    # return rho

    # if xctype == 'GGA':
    #     ngrids, nao = ao[0].shape
    # else:
    #     ngrids, nao = ao.shape

    # if non0tab is None:
    #     print "this PATH"
    #     non0tab = numpy.ones(((ngrids+BLKSIZE-1)//BLKSIZE,mol.nbas),
    #                          dtype=numpy.int8)

    # #if ao[0].dtype==numpy.complex128: # complex orbitals
    # if True:
    #     #dm_re = numpy.ascontiguousarray(dm.real)
    #     dm_re = dm
    #     rho = numpy.empty((4,ngrids))
    #     #ao_re = numpy.ascontiguousarray(ao[0].real)
    #     ao_re = ao[0]
    #     c0_rr = _dot_ao_dm(mol, ao_re, dm_re, nao, ngrids, non0tab)

    #     rho[0] = (numpy.einsum('pi,pi->p', ao_re, c0_rr))

    #     for i in range(1, 4):
    #         c1 = _dot_ao_dm(mol, ao[i], dm, nao, ngrids, non0tab)
    #         rho[i] = numpy.einsum('pi,pi->p', ao[0], c1) * 2 # *2 for +c.c.

    #     for i in range(1, 4):
    #     #     #ao_re = numpy.ascontiguousarray(ao[i].real)
    #         ao_re = ao[i]

    #         c1_rr = _dot_ao_dm(mol, ao_re, dm_re, nao, ngrids, non0tab)

    #         rho[i] = (numpy.einsum('pi,pi->p', ao_re, c1_rr)) *2
    #     return rho

    # complex orbitals or density matrix
    if numpy.iscomplexobj(ao) or numpy.iscomplexobj(dm):

        dm_re = numpy.ascontiguousarray(dm.real)
        dm_im = numpy.ascontiguousarray(dm.imag)

        if xctype == 'GGA':
            rho = numpy.empty((4,ngrids))
            ao0_re = numpy.ascontiguousarray(ao[0].real)
            ao0_im = numpy.ascontiguousarray(ao[0].imag)

            # DM * ket: e.g. ir denotes dm_im | ao_re >
            c0_rr = _dot_ao_dm(mol, ao0_re, dm_re, nao, ngrids, non0tab)
            c0_ri = _dot_ao_dm(mol, ao0_im, dm_re, nao, ngrids, non0tab)
            c0_ir = _dot_ao_dm(mol, ao0_re, dm_im, nao, ngrids, non0tab)
            c0_ii = _dot_ao_dm(mol, ao0_im, dm_im, nao, ngrids, non0tab)

            # bra * DM
            rho[0] = (numpy.einsum('pi,pi->p', ao0_im, c0_ri) +
                      numpy.einsum('pi,pi->p', ao0_re, c0_rr) +
                      numpy.einsum('pi,pi->p', ao0_im, c0_ir) -
                      numpy.einsum('pi,pi->p', ao0_re, c0_ii))

            for i in range(1, 4):
                # ao_re = numpy.ascontiguousarray(ao[i].real)
                # ao_im = numpy.ascontiguousarray(ao[i].imag)
                ao_re = numpy.ascontiguousarray(ao[i].real)
                ao_im = numpy.ascontiguousarray(ao[i].imag)

                c1_rr = _dot_ao_dm(mol, ao_re, dm_re, nao, ngrids, non0tab)
                c1_ri = _dot_ao_dm(mol, ao_im, dm_re, nao, ngrids, non0tab)
                c1_ir = _dot_ao_dm(mol, ao_re, dm_im, nao, ngrids, non0tab)
                c1_ii = _dot_ao_dm(mol, ao_im, dm_im, nao, ngrids, non0tab)

                rho[i] = (numpy.einsum('pi,pi->p', ao0_im, c1_ri) +
                          numpy.einsum('pi,pi->p', ao0_re, c1_rr) +
                          numpy.einsum('pi,pi->p', ao0_im, c1_ir) -
                          numpy.einsum('pi,pi->p', ao0_re, c1_ii)) * 2 # *2 for +c.c.
        else:
            ao_re = numpy.ascontiguousarray(ao.real)
            ao_im = numpy.ascontiguousarray(ao.imag)
            # DM * ket: e.g. ir denotes dm_im | ao_re >

            c0_rr = _dot_ao_dm(mol, ao_re, dm_re, nao, ngrids, non0tab)
            c0_ri = _dot_ao_dm(mol, ao_im, dm_re, nao, ngrids, non0tab)
            c0_ir = _dot_ao_dm(mol, ao_re, dm_im, nao, ngrids, non0tab)
            c0_ii = _dot_ao_dm(mol, ao_im, dm_im, nao, ngrids, non0tab)
            # bra * DM
            rho = (numpy.einsum('pi,pi->p', ao_im, c0_ri) +
                   numpy.einsum('pi,pi->p', ao_re, c0_rr) +
                   numpy.einsum('pi,pi->p', ao_im, c0_ir) -
                   numpy.einsum('pi,pi->p', ao_re, c0_ii))

    # real orbitals and real DM
    else:
        rho = pyscf.dft.numint.eval_rho(mol, ao, dm, non0tab, xctype, verbose)

    return rho
Пример #12
0
def eval_rho(mol, ao, dm, non0tab=None, xctype='LDA', hermi=0,
             with_lapl=True, verbose=None):
    '''Calculate the electron density and magnetization spin density in the
    framework of 2-component real basis.
    ''' + numint.eval_rho.__doc__
    nao = ao.shape[-1]
    assert dm.ndim == 2 and nao * 2 == dm.shape[0]

    ngrids, nao = ao.shape[-2:]
    xctype = xctype.upper()
    if non0tab is None:
        non0tab = np.ones(((ngrids+BLKSIZE-1)//BLKSIZE,mol.nbas),
                          dtype=np.uint8)
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc

    if xctype == 'LDA':
        c0a = _dot_ao_dm(mol, ao, dm[:nao], non0tab, shls_slice, ao_loc)
        c0b = _dot_ao_dm(mol, ao, dm[nao:], non0tab, shls_slice, ao_loc)
        rho_m = _contract_rho_m((ao, ao), (c0a, c0b), hermi, True)
    elif xctype == 'GGA':
        # first 4 ~ (rho, m), second 4 ~ (0th order, dx, dy, dz)
        if hermi:
            rho_m = np.empty((4, 4, ngrids))
        else:
            rho_m = np.empty((4, 4, ngrids), dtype=np.complex128)
        c0a = _dot_ao_dm(mol, ao[0], dm[:nao], non0tab, shls_slice, ao_loc)
        c0b = _dot_ao_dm(mol, ao[0], dm[nao:], non0tab, shls_slice, ao_loc)
        c0 = (c0a, c0b)
        rho_m[:,0] = _contract_rho_m((ao[0], ao[0]), c0, hermi, True)
        for i in range(1, 4):
            rho_m[:,i] = _contract_rho_m((ao[i], ao[i]), c0, hermi, False)
        if hermi:
            rho_m[:,1:4] *= 2  # *2 for |ao> dm < dx ao| + |dx ao> dm < ao|
        else:
            for i in range(1, 4):
                c1a = _dot_ao_dm(mol, ao[i], dm[:nao], non0tab, shls_slice, ao_loc)
                c1b = _dot_ao_dm(mol, ao[i], dm[nao:], non0tab, shls_slice, ao_loc)
                rho_m[:,i] += _contract_rho_m((ao[0], ao[0]), (c1a, c1b), hermi, False)
    else: # meta-GGA
        if hermi:
            dtype = np.double
        else:
            dtype = np.complex128
        if with_lapl:
            rho_m = np.empty((4, 6, ngrids), dtype=dtype)
            tau_idx = 5
        else:
            rho_m = np.empty((4, 5, ngrids), dtype=dtype)
            tau_idx = 4
        c0a = _dot_ao_dm(mol, ao[0], dm[:nao], non0tab, shls_slice, ao_loc)
        c0b = _dot_ao_dm(mol, ao[0], dm[nao:], non0tab, shls_slice, ao_loc)
        c0 = (c0a, c0b)
        rho_m[:,0] = _contract_rho_m((ao[0], ao[0]), c0, hermi, True)
        rho_m[:,tau_idx] = 0
        for i in range(1, 4):
            c1a = _dot_ao_dm(mol, ao[i], dm[:nao], non0tab, shls_slice, ao_loc)
            c1b = _dot_ao_dm(mol, ao[i], dm[nao:], non0tab, shls_slice, ao_loc)
            rho_m[:,tau_idx] += _contract_rho_m((ao[i], ao[i]), (c1a, c1b), hermi, True)

            rho_m[:,i] = _contract_rho_m((ao[i], ao[i]), c0, hermi, False)
            if hermi:
                rho_m[:,i] *= 2
            else:
                rho_m[:,i] += _contract_rho_m((ao[0], ao[0]), (c1a, c1b), hermi, False)
        if with_lapl:
            # TODO: rho_m[:,4] = \nabla^2 rho
            raise NotImplementedError
        # tau = 1/2 (\nabla f)^2
        rho_m[:,tau_idx] *= .5
    return rho_m
Пример #13
0
Файл: rks.py Проект: pyscf/pyscf
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    v_ip = numpy.zeros((3, nao, nao))
    vmat = numpy.zeros((mol.natm, 3, nao, nao))
    max_memory = max(2000, max_memory - vmat.size * 8 / 1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            aow = numint._scale_ao(ao[0], weight * vrho)
            rks_grad._d1_dot_(v_ip, mol, ao[1:4], aow, mask, ao_loc, True)

            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice,
                                       ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # First order density = rho1 * 2.  *2 is not applied because + c.c. in the end
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                    ao_dm0[:, p0:p1])
                wv = weight * frr * rho1
                aow = [numint._scale_ao(ao[0], wv[i]) for i in range(3)]
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc,
                                  True)
            ao_dm0 = aow = None

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            rks_grad._gga_grad_sum_(v_ip, mol, ao, wv, mask, ao_loc)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)
                aow = [numint._scale_ao(ao[:4], wv[i, :4]) for i in range(3)]
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc,
                                  True)
            ao_dm0 = aow = None

    elif xctype == 'MGGA':
        if grids.level < 5:
            logger.warn(mol, 'MGGA Hessian is sensitive to dft grids.')
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:10], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_mgga_wv0(rho, vxc, weight)
            rks_grad._gga_grad_sum_(v_ip, mol, ao, wv, mask, ao_loc)

            # *2 because wv[5] is scaled by 0.5 in _rks_mgga_wv0
            rks_grad._tau_grad_dot_(v_ip, mol, ao, wv[5] * 2, mask, ao_loc,
                                    True)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices, xctype)
                wv[0] = numint._rks_mgga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_mgga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_mgga_wv1(rho, dR_rho1[2], vxc, fxc, weight)
                aow = [numint._scale_ao(ao[:4], wv[i, :4]) for i in range(3)]
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc,
                                  True)

                for j in range(1, 4):
                    aow = [numint._scale_ao(ao[j], wv[i, 5]) for i in range(3)]
                    rks_grad._d1_dot_(vmat[ia], mol, aow, ao[j], mask, ao_loc,
                                      True)
            ao_dm0 = aow = None

    for ia in range(mol.natm):
        p0, p1 = aoslices[ia][2:]
        vmat[ia, :, p0:p1] += v_ip[:, p0:p1]
        vmat[ia] = -vmat[ia] - vmat[ia].transpose(0, 2, 1)

    return vmat
Пример #14
0
Файл: rks.py Проект: pyscf/pyscf
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    vmat = numpy.zeros((mol.natm, 3, 3, nao, nao))
    ipip = numpy.zeros((3, 3, nao, nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            wv = weight * vrho
            aow = [numint._scale_ao(ao[i], wv) for i in range(1, 4)]
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice,
                                       ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                    ao_dm0[:, p0:p1]) * 2
                # aow ~ rho1 ~ d/dR1
                wv = weight * frr * rho1
                aow = [numint._scale_ao(ao[0], wv[i]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, :, p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wv)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                for i in range(3):
                    wv[i] = numint._rks_gga_wv1(rho, dR_rho1[i], vxc, fxc,
                                                weight)
                    aow = rks_grad._make_dR_dao_w(ao, wv[i])
                    rks_grad._d1_dot_(vmat[ia, i], mol, aow, ao[0], mask,
                                      ao_loc, True)

                aow = [numint._scale_ao(ao[:4], wv[i, :4]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, :, p0:p1]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, p0:p1].transpose(1, 0, 3, 2)

    elif xctype == 'MGGA':
        XX, XY, XZ = 4, 5, 6
        YX, YY, YZ = 5, 7, 8
        ZX, ZY, ZZ = 6, 8, 9
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:10], mo_coeff, mo_occ, mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_mgga_wv0(rho, vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wv)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            aow = [numint._scale_ao(ao[i], wv[5]) for i in range(4, 10)]
            _d1d2_dot_(ipip, mol, [aow[0], aow[1], aow[2]],
                       [ao[XX], ao[XY], ao[XZ]], mask, ao_loc, False)
            _d1d2_dot_(ipip, mol, [aow[1], aow[3], aow[4]],
                       [ao[YX], ao[YY], ao[YZ]], mask, ao_loc, False)
            _d1d2_dot_(ipip, mol, [aow[2], aow[4], aow[5]],
                       [ao[ZX], ao[ZY], ao[ZZ]], mask, ao_loc, False)

            ao_dm0 = [
                numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices, xctype)
                for i in range(3):
                    wv[i] = numint._rks_mgga_wv1(rho, dR_rho1[i], vxc, fxc,
                                                 weight)
                    aow = rks_grad._make_dR_dao_w(ao, wv[i])
                    rks_grad._d1_dot_(vmat[ia, i], mol, aow, ao[0], mask,
                                      ao_loc, True)

                aow = [numint._scale_ao(ao[:4], wv[i, :4]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)

                # *2 because wv[5] is scaled by 0.5 in _rks_mgga_wv1
                wv[:, 5] *= 2
                aow = [numint._scale_ao(ao[1], wv[i, 5]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, [ao[XX], ao[XY], ao[XZ]], aow, mask,
                           ao_loc, False)
                aow = [numint._scale_ao(ao[2], wv[i, 5]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, [ao[YX], ao[YY], ao[YZ]], aow, mask,
                           ao_loc, False)
                aow = [numint._scale_ao(ao[3], wv[i, 5]) for i in range(3)]
                _d1d2_dot_(vmat[ia], mol, [ao[ZX], ao[ZY], ao[ZZ]], aow, mask,
                           ao_loc, False)

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, :, p0:p1]
            vmat[ia, :, :, :, p0:p1] += ipip[:, :, p0:p1].transpose(1, 0, 3, 2)

    return vmat
Пример #15
0
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    """" This functions is slightly different from hessian.rks._get_vxc_deriv1 in that <\nabla u|Vxc|v> is removed"""
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)
    vmat = np.zeros((mol.natm,3,nao,nao))
    max_memory = max(2000, max_memory-vmat.size*8/1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, 'LDA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            frr = fxc[0]
            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice, ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                rho1 = np.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0[:,p0:p1])
                aow = np.einsum('pi,xp->xpi', ao[0], weight*frr*rho1)
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0,2,1)

    elif xctype == 'GGA':
        ao_deriv = 2
        # v_ip = np.zeros((3,nao,nao))
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, 'GGA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            # rks_grad._gga_grad_sum_(v_ip, mol, ao, wv, mask, ao_loc)
            ao_dm0 = [numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                      for i in range(4)]
            for ia in range(mol.natm):
                wv = dR_rho1 = rks_hess._make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)
                aow = np.einsum('npi,Xnp->Xpi', ao[:4], wv)
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0,2,1)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmat
Пример #16
0
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff[0].shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)

    vmata = numpy.zeros((mol.natm,3,nao,nao))
    vmatb = numpy.zeros((mol.natm,3,nao,nao))
    max_memory = max(2000, max_memory-(vmata.size+vmatb.size)*8/1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]
            vrho = vxc[0]
            u_u, u_d, d_d = fxc[0].T

            ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
            ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
            aow1a = numpy.einsum('xpi,p->xpi', ao[1:], weight*vrho[:,0])
            aow1b = numpy.einsum('xpi,p->xpi', ao[1:], weight*vrho[:,1])
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
# First order density = rho1 * 2.  *2 is not applied because + c.c. in the end
                rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1])
                rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1])

                wv = u_u * rho1a + u_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                aow[:,:,p0:p1] += aow1a[:,:,p0:p1]
                rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)

                wv = u_d * rho1a + d_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                aow[:,:,p0:p1] += aow1b[:,:,p0:p1]
                rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0a = ao_dm0b = aow = aow1a = aow1b = None

        for ia in range(mol.natm):
            vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
            vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)

    elif xctype == 'GGA':
        ao_deriv = 2
        vipa = numpy.zeros((3,nao,nao))
        vipb = numpy.zeros((3,nao,nao))
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_gga_wv0((rhoa,rhob), vxc, weight)
            rks_grad._gga_grad_sum_(vipa, mol, ao, wva, mask, ao_loc)
            rks_grad._gga_grad_sum_(vipb, mol, ao, wvb, mask, ao_loc)

            ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                       for i in range(4)]
            ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                       for i in range(4)]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices)
                wva[0], wvb[0] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[0],dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[1],dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[2],dR_rho1b[2]), vxc, fxc, weight)

                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wva)
                rks_grad._d1_dot_(vmata[ia], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wvb)
                rks_grad._d1_dot_(vmatb[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,p0:p1] += vipa[:,p0:p1]
            vmata[ia] = -vmata[ia] - vmata[ia].transpose(0,2,1)
            vmatb[ia,:,p0:p1] += vipb[:,p0:p1]
            vmatb[ia] = -vmatb[ia] - vmatb[ia].transpose(0,2,1)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmata, vmatb
Пример #17
0
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff[0].shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)

    vmata = numpy.zeros((mol.natm,3,3,nao,nao))
    vmatb = numpy.zeros((mol.natm,3,3,nao,nao))
    ipipa = numpy.zeros((3,3,nao,nao))
    ipipb = numpy.zeros((3,3,nao,nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]
            vrho = vxc[0]
            u_u, u_d, d_d = fxc[0].T

            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight*vrho[:,0])
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight*vrho[:,1])
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice, ao_loc)
            ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice, ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1a = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0a[:,p0:p1]) * 2
                rho1b = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0b[:,p0:p1]) * 2

                wv = u_u * rho1a + u_d * rho1b
                wv *= weight
                # aow ~ rho1 ~ d/dR1
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)

                wv = u_d * rho1a + d_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask, xctype)
            rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask, xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa,rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_gga_wv0((rhoa,rhob), vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wva)
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = rks_grad._make_dR_dao_w(ao, wvb)
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = [numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                       for i in range(4)]
            ao_dm0b = [numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                       for i in range(4)]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(ao, ao_dm0a, ia, aoslices)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(ao, ao_dm0b, ia, aoslices)
                wva[0], wvb[0] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[0],dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[1],dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_gga_wv1((rhoa,rhob), (dR_rho1a[2],dR_rho1b[2]), vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wva[0])
                rks_grad._d1_dot_(vmata[ia,0], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wva[1])
                rks_grad._d1_dot_(vmata[ia,1], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wva[2])
                rks_grad._d1_dot_(vmata[ia,2], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wva)
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc, False)

                aow = rks_grad._make_dR_dao_w(ao, wvb[0])
                rks_grad._d1_dot_(vmatb[ia,0], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[1])
                rks_grad._d1_dot_(vmatb[ia,1], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[2])
                rks_grad._d1_dot_(vmatb[ia,2], mol, aow, ao[0], mask, ao_loc, True)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wvb)
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,:,p0:p1]
            vmata[ia,:,:,:,p0:p1] += ipipa[:,:,p0:p1].transpose(1,0,3,2)
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,:,p0:p1]
            vmatb[ia,:,:,:,p0:p1] += ipipb[:,:,p0:p1].transpose(1,0,3,2)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmata, vmatb
Пример #18
0
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    vmat = numpy.zeros((mol.natm,3,3,nao,nao))
    ipip = numpy.zeros((3,3,nao,nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, 'LDA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight*vrho)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice, ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0[:,p0:p1]) * 2
                # aow ~ rho1 ~ d/dR1
                aow = numpy.einsum('pi,xp->xpi', ao[0], weight*frr*rho1)
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia,:,:,:,p0:p1] += ipip[:,:,:,p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, 'GGA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wv)
            _d1d2_dot_(ipip, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0 = [numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                      for i in range(4)]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wv[0])
                rks_grad._d1_dot_(vmat[ia,0], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wv[1])
                rks_grad._d1_dot_(vmat[ia,1], mol, aow, ao[0], mask, ao_loc, True)
                aow = rks_grad._make_dR_dao_w(ao, wv[2])
                rks_grad._d1_dot_(vmat[ia,2], mol, aow, ao[0], mask, ao_loc, True)

                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wv)
                _d1d2_dot_(vmat[ia], mol, ao[1:4], aow, mask, ao_loc, False)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia,:,:,:,p0:p1] += ipip[:,:,:,p0:p1]
            vmat[ia,:,:,:,p0:p1] += ipip[:,:,p0:p1].transpose(1,0,3,2)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmat
Пример #19
0
def _get_vxc_deriv1(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff.shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0 = mf.make_rdm1(mo_coeff, mo_occ)

    vmat = numpy.zeros((mol.natm,3,nao,nao))
    max_memory = max(2000, max_memory-vmat.size*8/1e6)
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[0], mo_coeff, mo_occ, mask, 'LDA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]
            vrho = vxc[0]
            frr = fxc[0]
            ao_dm0 = numint._dot_ao_dm(mol, ao[0], dm0, mask, shls_slice, ao_loc)
            aow1 = numpy.einsum('xpi,p->xpi', ao[1:], weight*vrho)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
# First order density = rho1 * 2.  *2 is not applied because + c.c. in the end
                rho1 = numpy.einsum('xpi,pi->xp', ao[1:,:,p0:p1], ao_dm0[:,p0:p1])
                aow = numpy.einsum('pi,xp->xpi', ao[0], weight*frr*rho1)
                aow[:,:,p0:p1] += aow1[:,:,p0:p1]
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0 = aow = aow1 = None

        for ia in range(mol.natm):
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0,2,1)

    elif xctype == 'GGA':
        ao_deriv = 2
        v_ip = numpy.zeros((3,nao,nao))
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rho = ni.eval_rho2(mol, ao[:4], mo_coeff, mo_occ, mask, 'GGA')
            vxc, fxc = ni.eval_xc(mf.xc, rho, 0, deriv=2)[1:3]

            wv = numint._rks_gga_wv0(rho, vxc, weight)
            rks_grad._gga_grad_sum_(v_ip, mol, ao, wv, mask, ao_loc)

            ao_dm0 = [numint._dot_ao_dm(mol, ao[i], dm0, mask, shls_slice, ao_loc)
                      for i in range(4)]
            for ia in range(mol.natm):
                wv = dR_rho1 = _make_dR_rho1(ao, ao_dm0, ia, aoslices)
                wv[0] = numint._rks_gga_wv1(rho, dR_rho1[0], vxc, fxc, weight)
                wv[1] = numint._rks_gga_wv1(rho, dR_rho1[1], vxc, fxc, weight)
                wv[2] = numint._rks_gga_wv1(rho, dR_rho1[2], vxc, fxc, weight)
                aow = numpy.einsum('npi,Xnp->Xpi', ao[:4], wv)
                rks_grad._d1_dot_(vmat[ia], mol, aow, ao[0], mask, ao_loc, True)
            ao_dm0 = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmat[ia,:,p0:p1] += v_ip[:,p0:p1]
            vmat[ia] = -vmat[ia] - vmat[ia].transpose(0,2,1)

    elif xctype == 'MGGA':
        raise NotImplementedError('meta-GGA')

    return vmat
Пример #20
0
def _get_vxc_deriv2(hessobj, mo_coeff, mo_occ, max_memory):
    mol = hessobj.mol
    mf = hessobj.base
    if hessobj.grids is not None:
        grids = hessobj.grids
    else:
        grids = mf.grids
    if grids.coords is None:
        grids.build(with_non0tab=True)

    nao, nmo = mo_coeff[0].shape
    ni = mf._numint
    xctype = ni._xc_type(mf.xc)
    aoslices = mol.aoslice_by_atom()
    shls_slice = (0, mol.nbas)
    ao_loc = mol.ao_loc_nr()
    dm0a, dm0b = mf.make_rdm1(mo_coeff, mo_occ)

    vmata = numpy.zeros((mol.natm, 3, 3, nao, nao))
    vmatb = numpy.zeros((mol.natm, 3, 3, nao, nao))
    ipipa = numpy.zeros((3, 3, nao, nao))
    ipipb = numpy.zeros((3, 3, nao, nao))
    if xctype == 'LDA':
        ao_deriv = 1
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[0], mo_coeff[0], mo_occ[0], mask,
                                xctype)
            rhob = ni.eval_rho2(mol, ao[0], mo_coeff[1], mo_occ[1], mask,
                                xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa, rhob), 1, deriv=2)[1:3]
            vrho = vxc[0]
            u_u, u_d, d_d = fxc[0].T

            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight * vrho[:, 0])
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = numpy.einsum('xpi,p->xpi', ao[1:4], weight * vrho[:, 1])
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = numint._dot_ao_dm(mol, ao[0], dm0a, mask, shls_slice,
                                        ao_loc)
            ao_dm0b = numint._dot_ao_dm(mol, ao[0], dm0b, mask, shls_slice,
                                        ao_loc)
            for ia in range(mol.natm):
                p0, p1 = aoslices[ia][2:]
                # *2 for \nabla|ket> in rho1
                rho1a = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                     ao_dm0a[:, p0:p1]) * 2
                rho1b = numpy.einsum('xpi,pi->xp', ao[1:, :, p0:p1],
                                     ao_dm0b[:, p0:p1]) * 2

                wv = u_u * rho1a + u_d * rho1b
                wv *= weight
                # aow ~ rho1 ~ d/dR1
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)

                wv = u_d * rho1a + d_d * rho1b
                wv *= weight
                aow = numpy.einsum('pi,xp->xpi', ao[0], wv)
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia, :, :, :, p0:p1] += ipipa[:, :, :, p0:p1]
            vmatb[ia, :, :, :, p0:p1] += ipipb[:, :, :, p0:p1]

    elif xctype == 'GGA':
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:4], mo_coeff[0], mo_occ[0], mask,
                                xctype)
            rhob = ni.eval_rho2(mol, ao[:4], mo_coeff[1], mo_occ[1], mask,
                                xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa, rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_gga_wv0((rhoa, rhob), vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wva)
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = rks_grad._make_dR_dao_w(ao, wvb)
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            ao_dm0a = [
                numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            ao_dm0b = [
                numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(
                    ao, ao_dm0a, ia, aoslices)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(
                    ao, ao_dm0b, ia, aoslices)
                wva[0], wvb[0] = numint._uks_gga_wv1(
                    (rhoa, rhob), (dR_rho1a[0], dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_gga_wv1(
                    (rhoa, rhob), (dR_rho1a[1], dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_gga_wv1(
                    (rhoa, rhob), (dR_rho1a[2], dR_rho1b[2]), vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wva[0])
                rks_grad._d1_dot_(vmata[ia, 0], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wva[1])
                rks_grad._d1_dot_(vmata[ia, 1], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wva[2])
                rks_grad._d1_dot_(vmata[ia, 2], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = [numint._scale_ao(ao[:4], wva[i, :4]) for i in range(3)]
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)

                aow = rks_grad._make_dR_dao_w(ao, wvb[0])
                rks_grad._d1_dot_(vmatb[ia, 0], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[1])
                rks_grad._d1_dot_(vmatb[ia, 1], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[2])
                rks_grad._d1_dot_(vmatb[ia, 2], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = [numint._scale_ao(ao[:4], wvb[i, :4]) for i in range(3)]
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)
            ao_dm0a = ao_dm0b = aow = None

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia, :, :, :, p0:p1] += ipipa[:, :, :, p0:p1]
            vmata[ia, :, :, :, p0:p1] += ipipa[:, :,
                                               p0:p1].transpose(1, 0, 3, 2)
            vmatb[ia, :, :, :, p0:p1] += ipipb[:, :, :, p0:p1]
            vmatb[ia, :, :, :, p0:p1] += ipipb[:, :,
                                               p0:p1].transpose(1, 0, 3, 2)

    elif xctype == 'MGGA':
        XX, XY, XZ = 4, 5, 6
        YX, YY, YZ = 5, 7, 8
        ZX, ZY, ZZ = 6, 8, 9
        ao_deriv = 2
        for ao, mask, weight, coords \
                in ni.block_loop(mol, grids, nao, ao_deriv, max_memory):
            rhoa = ni.eval_rho2(mol, ao[:10], mo_coeff[0], mo_occ[0], mask,
                                xctype)
            rhob = ni.eval_rho2(mol, ao[:10], mo_coeff[1], mo_occ[1], mask,
                                xctype)
            vxc, fxc = ni.eval_xc(mf.xc, (rhoa, rhob), 1, deriv=2)[1:3]

            wva, wvb = numint._uks_mgga_wv0((rhoa, rhob), vxc, weight)
            aow = rks_grad._make_dR_dao_w(ao, wva)
            rks_hess._d1d2_dot_(ipipa, mol, aow, ao[1:4], mask, ao_loc, False)
            aow = rks_grad._make_dR_dao_w(ao, wvb)
            rks_hess._d1d2_dot_(ipipb, mol, aow, ao[1:4], mask, ao_loc, False)

            aow = [numint._scale_ao(ao[i], wva[5]) for i in range(4, 10)]
            rks_hess._d1d2_dot_(ipipa, mol, [aow[0], aow[1], aow[2]],
                                [ao[XX], ao[XY], ao[XZ]], mask, ao_loc, False)
            rks_hess._d1d2_dot_(ipipa, mol, [aow[1], aow[3], aow[4]],
                                [ao[YX], ao[YY], ao[YZ]], mask, ao_loc, False)
            rks_hess._d1d2_dot_(ipipa, mol, [aow[2], aow[4], aow[5]],
                                [ao[ZX], ao[ZY], ao[ZZ]], mask, ao_loc, False)
            aow = [numint._scale_ao(ao[i], wvb[5]) for i in range(4, 10)]
            rks_hess._d1d2_dot_(ipipb, mol, [aow[0], aow[1], aow[2]],
                                [ao[XX], ao[XY], ao[XZ]], mask, ao_loc, False)
            rks_hess._d1d2_dot_(ipipb, mol, [aow[1], aow[3], aow[4]],
                                [ao[YX], ao[YY], ao[YZ]], mask, ao_loc, False)
            rks_hess._d1d2_dot_(ipipb, mol, [aow[2], aow[4], aow[5]],
                                [ao[ZX], ao[ZY], ao[ZZ]], mask, ao_loc, False)

            ao_dm0a = [
                numint._dot_ao_dm(mol, ao[i], dm0a, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            ao_dm0b = [
                numint._dot_ao_dm(mol, ao[i], dm0b, mask, shls_slice, ao_loc)
                for i in range(4)
            ]
            for ia in range(mol.natm):
                wva = dR_rho1a = rks_hess._make_dR_rho1(
                    ao, ao_dm0a, ia, aoslices, xctype)
                wvb = dR_rho1b = rks_hess._make_dR_rho1(
                    ao, ao_dm0b, ia, aoslices, xctype)
                wva[0], wvb[0] = numint._uks_mgga_wv1(
                    (rhoa, rhob), (dR_rho1a[0], dR_rho1b[0]), vxc, fxc, weight)
                wva[1], wvb[1] = numint._uks_mgga_wv1(
                    (rhoa, rhob), (dR_rho1a[1], dR_rho1b[1]), vxc, fxc, weight)
                wva[2], wvb[2] = numint._uks_mgga_wv1(
                    (rhoa, rhob), (dR_rho1a[2], dR_rho1b[2]), vxc, fxc, weight)

                aow = rks_grad._make_dR_dao_w(ao, wva[0])
                rks_grad._d1_dot_(vmata[ia, 0], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wva[1])
                rks_grad._d1_dot_(vmata[ia, 1], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wva[2])
                rks_grad._d1_dot_(vmata[ia, 2], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = [numint._scale_ao(ao[:4], wva[i, :4]) for i in range(3)]
                rks_hess._d1d2_dot_(vmata[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)

                aow = rks_grad._make_dR_dao_w(ao, wvb[0])
                rks_grad._d1_dot_(vmatb[ia, 0], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[1])
                rks_grad._d1_dot_(vmatb[ia, 1], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = rks_grad._make_dR_dao_w(ao, wvb[2])
                rks_grad._d1_dot_(vmatb[ia, 2], mol, aow, ao[0], mask, ao_loc,
                                  True)
                aow = [numint._scale_ao(ao[:4], wvb[i, :4]) for i in range(3)]
                rks_hess._d1d2_dot_(vmatb[ia], mol, ao[1:4], aow, mask, ao_loc,
                                    False)

                # *2 because wv[5] is scaled by 0.5 in _rks_mgga_wv1
                wv = wva[:, 5] * 2
                aow = [numint._scale_ao(ao[1], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmata[ia], mol, [ao[XX], ao[XY], ao[XZ]],
                                    aow, mask, ao_loc, False)
                aow = [numint._scale_ao(ao[2], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmata[ia], mol, [ao[YX], ao[YY], ao[YZ]],
                                    aow, mask, ao_loc, False)
                aow = [numint._scale_ao(ao[3], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmata[ia], mol, [ao[ZX], ao[ZY], ao[ZZ]],
                                    aow, mask, ao_loc, False)
                wv = wvb[:, 5] * 2
                aow = [numint._scale_ao(ao[1], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[XX], ao[XY], ao[XZ]],
                                    aow, mask, ao_loc, False)
                aow = [numint._scale_ao(ao[2], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[YX], ao[YY], ao[YZ]],
                                    aow, mask, ao_loc, False)
                aow = [numint._scale_ao(ao[3], wv[i]) for i in range(3)]
                rks_hess._d1d2_dot_(vmatb[ia], mol, [ao[ZX], ao[ZY], ao[ZZ]],
                                    aow, mask, ao_loc, False)

        for ia in range(mol.natm):
            p0, p1 = aoslices[ia][2:]
            vmata[ia, :, :, :, p0:p1] += ipipa[:, :, :, p0:p1]
            vmata[ia, :, :, :, p0:p1] += ipipa[:, :,
                                               p0:p1].transpose(1, 0, 3, 2)
            vmatb[ia, :, :, :, p0:p1] += ipipb[:, :, :, p0:p1]
            vmatb[ia, :, :, :, p0:p1] += ipipb[:, :,
                                               p0:p1].transpose(1, 0, 3, 2)

    return vmata, vmatb