RESEARCH
Computational Spectroscopy of Condensed-Phase Systems
Vibrational spectroscopy is a powerful and sensitive tool to study structure and dynamics of many condensed-phase systems. Our group develops new and improves existing theoretical methods for linear and nonlinear infrared, sum-frequency generation, and Raman spectroscopies of condensed-phase systems of biological and technological importance such as liquid water, ice, ionic liquids, proteins in solution, on surfaces and at the interfaces.
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Representative publications:
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Probing ion configurations in the KcsA selectivity filter with single isotope labels and 2D IR spectroscopy, M. Ryan, L. Gao, F. Valiyaveetil, M. T. Zanni, and A. A. Kananenka, Journal of the American Chemical Society 145, 18529–18537 (2023)
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Combinational vibration modes in H2O/HDO/D2O mixtures detected thanks to the superior sensitivity of femtosecond stimulated Raman scattering, M. Pastorczak, K. Duk, S. Shahab, and A. A. Kananenka, The Journal of Physical Chemistry B 127, 4843–4857 (2023)
Quantum Dynamics
Our group develops various machine learning approaches to a multitude of problems in atomic and molecular physics. Specifically, we utilize neural networks to establish structure-property relationships for computational spectroscopy, many-electron perturbation theory, and to analyze the diffraction data. We also use neural networks to study reduced dynamics of open quantum systems.
Representative publications:
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A comparative study of different machine learning methods for dissipative quantum dynamics, L. E. H. Rodriguez, A. Ullah, K. J. R. Espinosa, P. O. Dral, and A. A. Kananenka, Machine Learning: Science and Technology 3, 045016 (2022)
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Convolutional neural networks for long time dissipative quantum dynamics, L. E. H. Rodriguez and A. A. Kananenka, The Journal of Physical Chemistry Letters 12, 2476–2483 (2021)
