Methods for excited states

Here we briefly overview the electronic structure methods supported by MLatom via interfaces:

  • TDDFT and TDA via interface to PySCF.

  • TDDFT via interface to Gaussian.

  • AIQM1 and other semi-empirical QM methods implemented in the MNDO program. The excited-state properties can be calculated with fast CIS or slow MR-CI approaches with the above Hamiltonians. Note that AIQM1 also requires installation of TorchANI and dftd4 programs.

  • CASSCF via the interface to COLUMBUS.

  • ADC(2) via interface to Turbomole.

These methods support calculations of various excited-state properties such as energies, forces, oscillator strengths, and, often, nonadiabatic couplings.

Example

An example below shows how to define an excited-state method and use it for single-point calculations. Here, we use an interfce to Gaussian to perform TD-DFT calculations. To request such calculations, simply add ‘TD- to your DFT functional, i.e., B3LYP will not work, but TD-B3LYP will work. If you need to define a solvent or add any other keywords to the input, you can do it by passing them to the method argument together with your TD-DFT keyword. Example below shows how to extract excitation energies and oscillator strengths. You can use the method instance you have setup (below b3lyp variable) to do other simulations as usual, e.g., geometry optimization, molecular dynamics, and calculate and plot UV/vis spectra.

import mlatom as ml

mol = ml.molecule.from_xyz_string('''3

O             0.0047037000647           0.0034825819509          -0.0024602018358
H             0.2765387552084           0.8860394374343           0.2512345704190
H             0.6029575451964          -0.2349220208587          -0.7112743681126
''')
b3lyp = ml.methods(method='TD-B3LYP/6-31G* SCRF(CPCM, Solvent=Acetonitrile)', program='gaussian', save_files_in_current_directory=True)
b3lyp.predict(molecule=mol, nstates=3, current_state=1) # making predictions for 3 states (S0, S1, and S2) and calculating properties (energies & gradients, population analysis) for S1 (numbering of states from 0).
print(mol.excitation_energies * ml.constants.hartree2eV) # they are in hartree, so here we convert to eV for convenience
print(mol.oscillator_strengths)

Citation

See our paper for more details (please also cite it if you use the corresponding features):