Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918119356760064 |
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| author | Andreichev, Valerii Käser, Silvan Bocanegra, Erica L. Salik, Madeeha Johnson, Mark A. Meuwly, Markus |
| author_facet | Andreichev, Valerii Käser, Silvan Bocanegra, Erica L. Salik, Madeeha Johnson, Mark A. Meuwly, Markus |
| contents | The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature at 1666 cm$^{-1}$ was correctly predicted from the VPT2 calculations. An unstructured band centered at 2940 cm$^{-1}$ superimposed on a broad background extending from 2600 to 3200 cm$^{-1}$ is assigned to the H-transfer motion. The broad background involves a multitude of combination bands but a major role is played by the COH-bend. For the deuterated species, VPT2 and molecular dynamics simulations provide equally convincing assignments, in particular for the framework modes. Finally, based on the new PES the tunneling splitting for H-transfer is predicted as $Δ_{\rm H} = 35.0$ cm$^{-1}$ from ring polymer instanton calculations using higher-order corrections. This provides an experimentally accessible benchmark to validate the computations, in particular the quality of the machine-learned PES. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings Andreichev, Valerii Käser, Silvan Bocanegra, Erica L. Salik, Madeeha Johnson, Mark A. Meuwly, Markus Chemical Physics The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature at 1666 cm$^{-1}$ was correctly predicted from the VPT2 calculations. An unstructured band centered at 2940 cm$^{-1}$ superimposed on a broad background extending from 2600 to 3200 cm$^{-1}$ is assigned to the H-transfer motion. The broad background involves a multitude of combination bands but a major role is played by the COH-bend. For the deuterated species, VPT2 and molecular dynamics simulations provide equally convincing assignments, in particular for the framework modes. Finally, based on the new PES the tunneling splitting for H-transfer is predicted as $Δ_{\rm H} = 35.0$ cm$^{-1}$ from ring polymer instanton calculations using higher-order corrections. This provides an experimentally accessible benchmark to validate the computations, in particular the quality of the machine-learned PES. |
| title | Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2508.06419 |