Predictive quantum vibrational spectra through active learning 4G-NNPs
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912745424683008 |
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| author | Faruque, Md Omar Limbu, Dil K. London, Nathan Momeni, Mohammad R. |
| author_facet | Faruque, Md Omar Limbu, Dil K. London, Nathan Momeni, Mohammad R. |
| contents | Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating non-local charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parameterizations and ad hoc fitting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01543 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive quantum vibrational spectra through active learning 4G-NNPs Faruque, Md Omar Limbu, Dil K. London, Nathan Momeni, Mohammad R. Materials Science Chemical Physics Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating non-local charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parameterizations and ad hoc fitting. |
| title | Predictive quantum vibrational spectra through active learning 4G-NNPs |
| topic | Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2511.01543 |