A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
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arXiv
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| Format: | Preprint |
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2025
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| author | Martinka, Jakub Zhang, Lina Hou, Yi-Fan Martyka, Mikołaj Pittner, Jiří Barbatti, Mario Dral, Pavlo O. |
| author_facet | Martinka, Jakub Zhang, Lina Hou, Yi-Fan Martyka, Mikołaj Pittner, Jiří Barbatti, Mario Dral, Pavlo O. |
| contents | Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH). There is therefore a strong incentive to machine learn NACs for accelerating simulations. However, this is challenging due to NACs' vectorial, double-valued character and the singularity near a conical intersection seam. For the first time, we design NAC-specific descriptors based on our domain expertise and show that they allow learning NACs with never-before-reported accuracy of $R^2$ exceeding 0.99. The key to success is also our new ML phase-correction procedure. We demonstrate the efficiency and robustness of our approach on a prototypical example of fully ML-driven FSSH simulations of fulvene targeting the SA-2-CASSCF(6,6) electronic structure level. This ML-FSSH dynamics leads to an accurate description of $S_1$ decay while reducing error bars by allowing the execution of a large ensemble of trajectories. Our implementations are available in open-source MLatom. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors Martinka, Jakub Zhang, Lina Hou, Yi-Fan Martyka, Mikołaj Pittner, Jiří Barbatti, Mario Dral, Pavlo O. Computational Physics Machine Learning Chemical Physics Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH). There is therefore a strong incentive to machine learn NACs for accelerating simulations. However, this is challenging due to NACs' vectorial, double-valued character and the singularity near a conical intersection seam. For the first time, we design NAC-specific descriptors based on our domain expertise and show that they allow learning NACs with never-before-reported accuracy of $R^2$ exceeding 0.99. The key to success is also our new ML phase-correction procedure. We demonstrate the efficiency and robustness of our approach on a prototypical example of fully ML-driven FSSH simulations of fulvene targeting the SA-2-CASSCF(6,6) electronic structure level. This ML-FSSH dynamics leads to an accurate description of $S_1$ decay while reducing error bars by allowing the execution of a large ensemble of trajectories. Our implementations are available in open-source MLatom. |
| title | A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors |
| topic | Computational Physics Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2505.23344 |