摘要： Understanding the interaction of light with molecules physisorbed on substrates is a fundamental problem in photonics, with applications in biosensing, photovoltaics, photocatalysis, plasmonics, and nanotechnology. However, the design of novel functional materials in silico is severely hampered by the lack of robust and computationally efficient methods for describing both molecular absorbance and screening on substrates. Here we employ our hybrid G0[W0 + ?W]-BSE implementation, which incorporates the substrate via its screening ?W at both the quasiparticle G0W0 level and when solving the Bethe-Salpeter equation (BSE). We show this method can be used to both efficiently and accurately describe the absorption spectra of physisorbed molecules on metal substrates and thereby tailor the molecule’s absorbance by altering the surface plasmon’s energy. Specifically, we investigate how the optical absorption spectra of three prototypical π-conjugated molecules: benzene (C6H6), terrylene (C30H16) and fullerene (C60), depends on the Wigner-Seitz radius rs of the metallic substrate. To gain further understanding of the light–molecule/substrate interaction, we also study the bright exciton’s electron and hole densities and their interactions with infrared active vibrational modes. Our results show that (1) benzene’s bright E1 2u exciton at 7.0 eV, whose energy is insensitive to changes in rs, could be relevant for photocatalytic dehydrogenation and polymerization reactions, (2) terrylene’s bright B3u exciton at 2.3 eV hybridizes with the surface plasmon, allowing the tailoring of the excitonic energy and optical activation of a surface plasmon-like exciton, and (3) fullerene’s π ? π? bright and dark excitons at 6.4 and 6.8 eV hybridize with the surface plasmon, resulting in the tailoring of their excitonic energy and the activation of both a surface plasmon-like exciton and a dark quadrupolar mode via symmetry breaking by the substrate. This work demonstrates how a proper description of interfacial light–molecular/substrate interactions enables the prediction, design, and optimization of technologically relevant phenomena in silico.