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Fragmentation dynamics shift dramatically from single molecules to dimers, revealing unexpected charge-separation pathways that redefine our understanding of strong-field photoionization.
Machine-learned potentials reveal that energized CF$_3$CHOO decomposes through unexpected pathways, challenging traditional reaction models.
Despite using fundamentally different machine learning architectures, PIP and PhysNet potentials yield remarkably consistent results for vibrational properties and tunneling dynamics, suggesting that both can reliably represent complex potential energy surfaces.
