Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects

• URL: http://arxiv.org/abs/2605.30165v1
• Date: Thu, 28 May 2026 16:18:49 GMT
• Title: Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects
• Abstract summary: 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 (kappa)<n>With exceptional fidelity, this framework reveals an anomalous high KIE-low kappa spanning 300-600 K, thereby defining a paradigm for the quantitative assessment of quantum tunneling.
• Score: 7.653249237286777
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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