Towards accurate excitation energies in supramolecular systems: combining T-CDFT and fragments in the BigDFT code
ORAL
Abstract
Despite the variety of available computational approaches, state-of-the-art methods for calculating excitation energies such as time-dependent density functional theory (TDDFT), are typically computationally demanding and thus limited to moderate system sizes. Thanks to a new variation of constrained DFT (CDFT), implemented in the BigDFT code and recently published by our group, we have shown to be able to robustly model both local and charge-transfer excitation energies with a comparable precision to TDDFT for local excitations, while not exhibiting the typical limits of standard TDDFT for charge-transfer states, for a computational cost close to ΔSCF. As T-CDFT is implemented within the linear scaing formalism it is naturally suited to be paired with the fragment approach already availalbe in the BigDFT code. By properly combining these two infrastructures, one can use TCDFT to impose excitations on particular fragments in supramolecular systems. In the example of local excitations on a molecule (fragment) in a given environment, where no strong coupling with the environment is expected, the constraint could be imposed between orbitals associated with the target fragment only, while still treating the full system at DFT level. In this talk I will show how this approach allows the exploration of explicit environment effects on excitation energies in systems such as TADF materials, paving the way for future simulations of excited states in realistic morphologies.
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Publication: Transition-based Constrained DFT for the robust and reliable treatment of excitations in molecular systems, M Stella, K Thapa, L Genovese and LE Ratcliff, J. Chem. Theory Comput. 2022, 18, 5, 3027–3038.
Presenters
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Martina Stella
The Abdus Salam International Centre for Theoretical Physics
Authors
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Martina Stella
The Abdus Salam International Centre for Theoretical Physics
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Luigi Genovese
CEA Grenoble
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William Dawson
RIKEN Adv Inst for Computational Science
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Laura Ratcliff
University of Bristol