Speaker
Description
Molecular response properties are central to the interpretation and design of functional systems for photonics, sensing, and nonlinear optical applications, yet their accurate description in the condensed phase remains challenging.[1–4] In such complex systems, the target molecule interacts with an environment ranging from transparent media, such as liquid solutions, to nanostructured substrates, such as metal nanoparticles.[1–4,6,8] A convenient route to address this complexity is to resort to quantum embedding methods, in which the full system is partitioned into interacting subsystems, allowing the region of primary interest to be treated at a high quantum-mechanical level while retaining an efficient and physically consistent description of the environment.[2–5,7] In solvated systems, the external radiation mainly interacts with the solute, while the external medium modifies the response through electrostatic, polarization, and short-range quantum effects.[1,3,4,8] In contrast, metal nanoparticles can absorb the electromagnetic radiation yielding to the collective excitation of localized surface plasmons, which strongly enhance the local field at the surface, and substantially modifying the electronic structure of molecular adsorbates.[6,8]
In this contribution, we present an integrated quantum-embedding/classical framework for molecular response properties in complex environments, ranging from solutions to nanostructured materials within a unified formalism.[3–8] The approach is based on multilevel density functional theory (MLDFT), in which the system is partitioned into active and inactive quantum subsystems described at the DFT level, to response theory through coupled-perturbed Kohn-Sham equations for the MLDFT Hamiltonian.[5,7] We showcase the accuracy of the approach for selected systems in solution, and we discuss the challenges for accurately describing hybrid molecule-plasmons systems, especially surface-enhanced spectral response.[4,6,8]
References:
[1] Tomasi, J; Mennucci, B; Cammi, R. Chem. Rev. 2005, 105, 2999-3094.
[2] Senn, H. M.; Thiel, W. Angew. Chem., Int. Ed. 2009, 48, 1198-1229.
[3] Giovannini, T; Egidi, F; Cappelli, C. Chem. Soc. Rev. 2020, 49, 5664-5677.
[4] Giovannini, T; Cappelli, C. Chem. Commun. 2023, 59, 5644-5660.
[5] Marrazzini, G.; Giovannini, T.; Scavino, M.; Egidi, F.; Cappelli, C.; Koch, H. J. Chem. Theory Comput. 2021, 17, 791-803.
[6] Lafiosca, P.; Nicoli, L.; Bonatti, L.; Giovannini, T.; Corni, S.; Cappelli, C. J. Chem. Theory Comput. 2023, 19, 3616-3633.
[7] Giovannini, T.; Scavino, M.; Koch, H. J. Chem. Theory Comput. 2024, 20, 3601-3612.
[8] Giovannini, T.; Gomez, S.; Cappelli, C. J. Phys. Chem. Lett. 2025, 16, 3106-3121.
Acknowledgments:
This work has received funding from the ERC under the European Union’s Horizon Europe research and innovation programme (grant no. 101219149, project CHOPIN). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or ERC Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.