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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.30165 |
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| _version_ | 1866917544614428672 |
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| author | Yang, Xinrui Wang, Zhigang |
| author_facet | Yang, Xinrui Wang, Zhigang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30165 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects Yang, Xinrui Wang, Zhigang Quantum Physics Chemical Physics Computational Physics 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. |
| title | Tunneling phase diagram: A machine-learning framework for multidimensional kinetic isotope effects |
| topic | Quantum Physics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2605.30165 |