Alieser Martinez
Institut Polytechnique des Sciences Avancées (IPSA) – Paris
This seminar presents recent research at the intersection of molecular photophysics, ultrafast dynamics, and surface science. In the first part, we explore the quantum and quasi-classical dynamics governing the physisorption, adsorption, and scattering of molecular hydrogen on nanostructured surfaces, including carbon-based networks, ZnO monolayers, and metallic substrates [1,2]. Dominant quantum effects are modeled using quantum liquid density functional theory, zero-point-energy-corrected quasi-classical trajectories, and interacting quantum trajectory representations [3,4]. We also address the computational modeling of catalytic surface processes, such as water splitting for green hydrogen production [5].
The second part focuses on light-induced ultrafast processes within complex organic architectures, such as carbon nanocages, nanorings, dendrimers, and molecular antennas [6,7]. Using non-adiabatic excited-state molecular dynamics simulations, we characterize vibronic couplings, electronic delocalization, and the mechanisms underlying energy transfer and exciton self-trapping. Furthermore, semiclassical modeling of ultrafast transient absorption spectroscopy—incorporating finite laser pulse duration effects—improves the agreement with experimental femtosecond photophysics data [8]. Finally, we detail ongoing efforts to move beyond established formalisms by integrating machine learning tools to scale these approaches to larger, more complex systems.
The second part focuses on light-induced ultrafast processes within complex organic architectures, such as carbon nanocages, nanorings, dendrimers, and molecular antennas [6,7]. Using non-adiabatic excited-state molecular dynamics simulations, we characterize vibronic couplings, electronic delocalization, and the mechanisms underlying energy transfer and exciton self-trapping. Furthermore, semiclassical modeling of ultrafast transient absorption spectroscopy—incorporating finite laser pulse duration effects—improves the agreement with experimental femtosecond photophysics data [8]. Finally, we detail ongoing efforts to move beyond established formalisms by integrating machine learning tools to scale these approaches to larger, more complex systems.
Bibliography
[1] R. Martin-Barrios, et al., J. Phys. Chem. C 125, 14075 (2021)
[2] R. Martin Barrios, et al., Eur. Phys. J. Spec. Top. 232, 1985 (2023)
[3] V.M. Freixas-Lemus, A. Martinez-Mesa, L. Uranga-Pina, Eur. Phys. J. Spec Top. 232, 1945 (2023)
[4] J.C. Acosta-Matos, C. Meier, A. Martinez-Mesa, L. Uranga-Piña, J. Phys. Chem. A 126, 1805 (2022)
[5] F. Rodriguez-Hernandez, et al., J. Phys. Chem. C 120, 25851 (2016)
[6] B. Rodriguez-Hernandez, et al., J. Phys. Chem. Lett. 12, 224 (2021)
[7] R. Perez-Castillo, et al., Chem. Sci. 15, 13250 (2024)
[8] R. Perez-Castillo, et al., Chem. Sci. 16, 13520 (2025)