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

Source: [Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects](<https://www.sota2.com/research/paper/6a191db8dbd7150506f8299c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-30T05:00:11.986Z

Authors: Xinrui Yang, Zhigang Wang

Published: 2026-05-28T16:18:49.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.30165>)

[PDF](<https://arxiv.org/pdf/2605.30165.pdf>)