Predictive quantum vibrational spectra through active learning 4G-NNPs

Md Omar Faruque; Dil K. Limbu; Nathan London; Mohammad R. Momeni

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arXiv · 2511.01543

Predictive quantum vibrational spectra through active learning 4G-NNPs

Md Omar Faruque·Dil K. Limbu·Nathan London·Mohammad R. Momeni

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Abstract

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.

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Md Omar Faruque, Dil K. Limbu, Nathan London, Mohammad R. Momeni. 2025-11-03. Predictive quantum vibrational spectra through active learning 4G-NNPs. https://arxiv.org/abs/2511.01543

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