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

Xinrui Yang; Zhigang Wang

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arXiv · 2605.30165

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

Xinrui Yang·Zhigang Wang

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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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Xinrui Yang, Zhigang Wang. 2026-05-28. Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects. https://arxiv.org/abs/2605.30165

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