Infrared spectra at coupled cluster accuracy from neural network representations

Abstract

Infrared spectroscopy is key to elucidate molecular structures, monitor reactions and observe conformational changes, while providing information on both structural and dynamical properties. This makes the accurate prediction of infrared spectra based on first-principle theories a highly desirable pursuit. Molecular dynamics simulations have proven to be a particularly powerful approach for this task, albeit requiring the computation of energies, forces and dipole moments for a large number of molecular configurations as a function of time. This explains why highly accurate first principles methods, such as coupled cluster theory, have so far been inapplicable for the prediction of fully anharmonic vibrational spectra of large systems at finite temperatures. Here, we push cutting-edge machine learning techniques forward by using neural network representations of energies, forces and in particular dipoles to predict such infrared spectra fully at "gold standard" coupled cluster accuracy as demonstrated for protonated water clusters as large as the protonated water hexamer, in its extended Zundel configuration. Furthermore, we show that this methodology can be used beyond the scope of the data considered during the development of the neural network models, allowing for the computation of finite-temperature infrared spectra of large systems inaccessible to explicit coupled cluster calculations. This substantially expands the hitherto existing limits of accuracy, speed and system size for theoretical spectroscopy and opens up a multitude of avenues for the prediction of vibrational spectra and the understanding of complex intra- and intermolecular couplings.

References in corpus (5)

• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Detection of Two Interstellar Polycyclic Aromatic Hydrocarbons via Spectral Matched Filtering
• Dynamics and Infrared Spectroscopy of the Protonated Water Dimer
• An Effective Semiclassical Approach to IR Spectroscopy
• Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer

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