(untitled) (Communications in Computational Chemistry, 2026)
Ultrafast optical field control has progressed from merely observing photochemical dynamics to actively steering molecular transformations. However, a persistent gap between theory and experiment continues to impede predictive control in complex systems. Femtosecond and attosecond techniques now allow real-time manipulation of electronic evolution, vibrational motions, and bond dissociation in small molecules. However, these achievements often fail to translate to condensed-phase environments due to increasing molecular complexity, environmental decoherence, and stringent instrumental constraints. This review summarizes recent advances in which pump-probe experiments—utilizing tailored pulse parameters such as intensity, wavelength, phase, and polarization—have uncovered key control mechanisms while also revealing critical challenges in scalability and reproducibility. Theoretical progress in quantum control methods (e.g., local control theory) and mixed quantum-classical simulations has clarified fundamental principles, yet often remains disconnected from experimental implementation. We propose that machine learning (ML) serves as an essential bridge to close this gap. By constructing hybrid theory-experiment databases and training environment-aware models, ML can capture complex pulse-branching correlations and system-environment couplings. These models effectively translate theoretical insights into experimentally feasible pulse designs, thereby paving the way for designing light-driven molecular processes that extend beyond gas-phase paradigms to functional materials.