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Add nonadiabatic coupling vector using TDDFT-ris (#451)
* add TDDFT-ris-nacv * add unit tests * add density-fitting support * fix some typos * fix some typos * debug finite difference ee * * add finite difference. * add unit tests. * add files * merge with master ris * Review the codes, according to comments. * fix some typos * change difficult-converged unit test to get_ab * add a unit test for df tddft-ris * change the basis * change the basis set * fix a bug
1 parent 25f91d3 commit fb6b6a3

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‎examples/34-tdhf-nacv.py renamed to ‎examples/34-tddft-nacv.py

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# limitations under the License.
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'''
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Nonadiabatic coupling vectors between ground and excited states for RHF
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Nonadiabatic coupling vectors for TDRHF and TDRKS
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'''
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# This example will gives the derivative coupling (DC),
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import pyscf
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import gpu4pyscf
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from gpu4pyscf.scf import hf
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from gpu4pyscf.dft import rks
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atom = '''
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O 0.0000000000 -0.0000000000 0.1174000000
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td = mf.TDA().set(nstates=5) # TDHF is OK
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td.kernel() # [ 9.21540892 10.99036172 11.83380819 13.62301694 15.06349085]
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nac = td.NAC()
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nac.state=(0,1) # same as (1,0) 0 means ground state, 1 means the first excited state
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nac = td.nac_method()
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nac.states=(0,1) # same as (1,0) 0 means ground state, 1 means the first excited state
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nac.kernel()
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'''
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--------- TDA nonadiabatic derivative coupling for state 0 and 1----------
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----------------------------------------------
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'''
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print('-----------------------------------------------------')
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print("Non-adiabatic coupling matrix element (NACME) between ground and first excited state")
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print(nac.de)
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print('-----------------------------------------------------')
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print("NACME between ground and first excited state scaled by E (/E_ex)")
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print(nac.de_scaled)
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print('-----------------------------------------------------')
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print("NACME between ground and first excited state with ETF (electron translation factor)")
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# Without including the contribution of the electron translation factor (ETF), for some molecules,
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# the non-adiabatic coupling matrix element (NACME) may lack translational invariance,
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# which can further lead to errors in subsequent calculations such as MD simulations.
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# In this case, it is necessary to use the NACME that takes the ETF into account.
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print(nac.de_etf)
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print('-----------------------------------------------------')
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print("NACME between ground and first excited state with ETF (electron translation factor) scaled by E (/E_ex)")
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print(nac.de_etf_scaled)
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mf = rks.RKS(mol, xc='b3lyp') # -76.4203783335521
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mf.kernel()
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td = mf.TDA().set(nstates=5) # TDHF is OK
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td.kernel() # [ 7.63727447 9.47865422 10.00032863 11.95971483 14.06564139]
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nac = td.nac_method()
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nac.states=(1,2) # same as (1,2) 1 means the first excited state, 2 means the second excited state
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nac.kernel()
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"""
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--------- TDA nonadiabatic derivative coupling for states 1 and 2----------
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x y z
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0 O -0.1134916788 -0.0000000000 0.0000000000
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1 H 0.0639158824 0.0000000000 0.0424664269
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2 H 0.0639158824 -0.0000000000 -0.0424664269
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 after E scaled (divided by E)----------
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x y z
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0 O -1.6771477392 -0.0000000000 0.0000000000
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1 H 0.9445307246 0.0000000000 0.6275567747
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2 H 0.9445307246 -0.0000000000 -0.6275567747
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF----------
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x y z
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0 O -0.1180169882 0.0000000000 0.0000000000
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1 H 0.0590085046 0.0000000000 0.0438508910
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2 H 0.0590085046 -0.0000000000 -0.0438508910
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF after E scaled (divided by E)----------
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x y z
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0 O -1.7440214738 0.0000000000 0.0000000000
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1 H 0.8720108929 0.0000000000 0.6480159906
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2 H 0.8720108929 -0.0000000000 -0.6480159906
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----------------------------------------------
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"""
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print('-----------------------------------------------------')
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print("Non-adiabatic coupling matrix element (NACME) between ground and first excited state")
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print(nac.de)

‎examples/37-tddft_ris_gradient.py

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#!/usr/bin/env python
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# Copyright 2021-2025 The PySCF Developers. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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'''
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Gradient for TDDFT-RIS
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'''
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import pyscf
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import numpy as np
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import gpu4pyscf
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from gpu4pyscf.dft import rks
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from gpu4pyscf.tdscf import ris
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atom = '''
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O 0.0000000000 -0.0000000000 0.1174000000
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H -0.7570000000 -0.0000000000 -0.4696000000
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H 0.7570000000 0.0000000000 -0.4696000000
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'''
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mol = pyscf.M(atom=atom, basis='def2tzvp')
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mf = rks.RKS(mol, xc='pbe0') # -76.3773133945678
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mf.kernel()
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td = mf.TDA()
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td.nstates=5
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td.kernel() # [ 7.81949919 9.71029362 10.13398432 12.10163229 13.93675959] (eV)
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g = td.nuc_grad_method()
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g.state=1
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g.kernel()
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"""
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--------- TDA gradients for state 1 ----------
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x y z
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0 O 0.0000000000 0.0000000000 -0.0949023769
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1 H 0.0627472634 -0.0000000000 0.0474538726
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2 H -0.0627472634 -0.0000000000 0.0474538726
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----------------------------------------------
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"""
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td_ris = ris.TDA(mf=mf.to_gpu(), nstates=5, spectra=False, single=False, gram_schmidt=True, Ktrunc=0.0)
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td_ris.conv_tol = 1.0E-4
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td_ris.kernel() # [ 7.56352157 9.65590899 10.1236409 12.09873137 13.921372 ] (eV)
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g_ris = td_ris.nuc_grad_method()
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g_ris.state=1
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g_ris.kernel()
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"""
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--------- TDA gradients for state 1 ----------
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x y z
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0 O 0.0000000106 -0.0000000000 -0.0969465222
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1 H 0.0674194961 0.0000000000 0.0484759423
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2 H -0.0674195066 0.0000000000 0.0484759501
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----------------------------------------------
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"""
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print("defference for excitation energy between TDA and TDA-ris (in eV)")
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print(td.e*27.21138602 - td_ris.energies.get())
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print()
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"""
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[0.25597762 0.05438464 0.01034341 0.00290092 0.01538759]
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"""
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print("defference for gradient between TDA and TDA-ris (in Hartree/Bohr)")
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print(g.de - g_ris.de)
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"""
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[[-1.05589088e-08 -1.97977506e-15 2.04414538e-03]
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[-4.67223270e-03 9.83637092e-16 -1.02206969e-03]
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[ 4.67224325e-03 1.00292678e-15 -1.02207751e-03]]
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"""
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print("norm of the diff")
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print(np.linalg.norm(g.de - g_ris.de))
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"""
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0.007065933384199997
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"""

‎examples/38-tddft_ris_nacv.py

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#!/usr/bin/env python
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# Copyright 2021-2025 The PySCF Developers. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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'''
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NACV for TDDFT-RIS
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'''
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# This example will gives the derivative coupling (DC),
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# also known as NACME (non-adiabatic coupling matrix element)
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# between ground and excited states.
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import numpy as np
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import pyscf
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import gpu4pyscf
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from gpu4pyscf.dft import rks
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from gpu4pyscf.tdscf import ris
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atom = '''
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O 0.0000000000 -0.0000000000 0.1174000000
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H -0.7570000000 -0.0000000000 -0.4696000000
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H 0.7570000000 0.0000000000 -0.4696000000
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'''
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mol = pyscf.M(atom=atom, basis='def2tzvp')
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mf = rks.RKS(mol, xc='pbe0') # -76.3773133945678
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mf.conv_tol = 1e-10
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mf.kernel()
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td = mf.TDA().set(nstates=5)
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td.kernel() # [ 7.81949919 9.71029362 10.13398432 12.10163229 13.93675959]
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nac = td.nac_method()
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nac.states=(1,2)
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nac.kernel()
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"""
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--------- TDA nonadiabatic derivative coupling for states 1 and 2----------
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x y z
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0 O -0.0975602441 -0.0000000000 -0.0000000000
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1 H 0.0548213338 -0.0000000000 0.0360881697
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2 H 0.0548213338 0.0000000000 -0.0360881697
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 after E scaled (divided by E)----------
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x y z
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0 O -1.4040391809 -0.0000000000 -0.0000000000
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1 H 0.7889617464 -0.0000000000 0.5193632370
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2 H 0.7889617464 0.0000000000 -0.5193632370
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF----------
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x y z
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0 O -0.0965494688 -0.0000000000 -0.0000000000
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1 H 0.0482746550 -0.0000000000 0.0378920920
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2 H 0.0482746550 0.0000000000 -0.0378920920
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF after E scaled (divided by E)----------
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x y z
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0 O -1.3894925990 -0.0000000000 -0.0000000000
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1 H 0.6947451570 -0.0000000000 0.5453244023
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2 H 0.6947451570 0.0000000000 -0.5453244023
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----------------------------------------------
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"""
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td_ris = ris.TDA(mf=mf.to_gpu(), nstates=5, spectra=False, single=False, gram_schmidt=True, Ktrunc=0.0)
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td_ris.conv_tol = 1.0E-6
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td_ris.kernel() # [ 7.56352157 9.65590898 10.12364072 12.09873136 13.921372 ]
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print(td_ris.energies.get())
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nac_ris = td_ris.nac_method()
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nac_ris.states=(1,2)
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nac_ris.kernel()
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"""
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--------- TDA nonadiabatic derivative coupling for states 1 and 2----------
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x y z
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0 O 0.1009731844 0.0000000000 -0.0000000014
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1 H -0.0575662857 -0.0000000000 -0.0380920213
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2 H -0.0575662879 0.0000000000 0.0380920227
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 after E scaled (divided by E)----------
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x y z
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0 O 1.3131508452 0.0000000000 -0.0000000181
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1 H -0.7486464564 -0.0000000000 -0.4953846935
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2 H -0.7486464849 0.0000000000 0.4953847117
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF----------
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x y z
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0 O 0.1006324741 0.0000000000 -0.0000000015
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1 H -0.0503161533 -0.0000000000 -0.0395091578
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2 H -0.0503161556 0.0000000000 0.0395091592
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--------- TDA nonadiabatic derivative coupling for states 1 and 2 with ETF after E scaled (divided by E)----------
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x y z
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0 O 1.3087199253 0.0000000000 -0.0000000189
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1 H -0.6543588731 -0.0000000000 -0.5138144769
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2 H -0.6543589035 0.0000000000 0.5138144958
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----------------------------------------------
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"""
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print("defference for excitation energy between TDA and TDA-ris (in eV)")
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print(td.e*27.21138602 - td_ris.energies.get())
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print()
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"""
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[0.25597762 0.05438464 0.01034359 0.00290093 0.01538759]
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"""
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print("CIS derivative coupling without ETF")
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print(np.abs(nac.de_scaled) - np.abs(nac_ris.de_scaled))
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print("norm of difference", np.linalg.norm(np.abs(nac.de_scaled) - np.abs(nac_ris.de_scaled)))
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print()
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"""
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[[ 9.08883357e-02 7.20409145e-15 -1.80700507e-08]
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[ 4.03152900e-02 1.15468620e-14 2.39785435e-02]
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[ 4.03152615e-02 1.77621884e-16 2.39785253e-02]]
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0.11252232092869598
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"""
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print("difference for CIS derivative coupling with ETF")
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print(np.abs(nac.de_etf_scaled) - np.abs(nac_ris.de_etf_scaled))
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print("norm of difference", np.linalg.norm(np.abs(nac.de_etf_scaled) - np.abs(nac_ris.de_etf_scaled)))
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print()
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"""
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[[ 8.07726737e-02 1.38040250e-14 -1.88986440e-08]
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[ 4.03862838e-02 1.32300610e-14 3.15099254e-02]
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[ 4.03862535e-02 -6.54111691e-16 3.15099065e-02]]
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0.10849919731015174
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"""
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‎gpu4pyscf/df/grad/tdrks_ris.py

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# Copyright 2021-2025 The PySCF Developers. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from gpu4pyscf.df import int3c2e, df
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from gpu4pyscf.df.grad import tdrhf as tdrhf_grad_df
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from gpu4pyscf.tdscf import ris
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from gpu4pyscf.grad import tdrks_ris as tdrks_ris_grad
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from gpu4pyscf import __config__
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class Gradients(tdrks_ris_grad.Gradients):
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from gpu4pyscf.lib.utils import to_gpu, device
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_keys = {'with_df', 'auxbasis_response'}
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def __init__(self, td):
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# Whether to include the response of DF auxiliary basis when computing
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# nuclear gradients of J/K matrices
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tdrks_ris_grad.Gradients.__init__(self, td)
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auxbasis_response = True
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get_jk = tdrhf_grad_df.get_jk
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def check_sanity(self):
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assert isinstance(self.base._scf, df.df_jk._DFHF)
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assert isinstance(self.base, ris.TDDFT) or isinstance(self.base, ris.TDA)
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get_veff = tdrhf_grad_df.get_veff
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Grad = Gradients

‎gpu4pyscf/df/nac/__init__.py

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# Copyright 2021-2024 The PySCF Developers. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from . import tdrhf, tdrks, tdrks_ris

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