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This paper introduces a machine-learning framework, the "tunneling phase diagram," to disentangle tunneling contributions from zero-point energy and classical kinetics within kinetic isotope effects (KIEs). The framework uses machine learning to decode the nonlinear relationship between KIE and the tunneling factor (魏), achieving high fidelity (R^2>0.98). Application of the framework reveals an anomalous region of high KIE but low 魏 between 300-600K, providing a quantitative measure of quantum tunneling.
Machine learning can now reveal hidden quantum tunneling effects within chemical reactions, even when masked by other kinetic factors.
The kinetic isotope effect (KIE) is the conventional probe for quantum tunneling, yet its composite nature conflates tunneling with zero-point energy and classical kinetics. Here, we introduce the tunneling phase diagram, a machine-learning framework that decouples true tunneling strength by decoding the nonlinear relationship between KIE and the tunneling factor (\k{appa}). With exceptional fidelity (R^2>0.98, RMSE = 0.21), this framework reveals an anomalous high KIE-low \k{appa} spanning 300-600 K, thereby defining a paradigm for the quantitative assessment of quantum tunneling.
