Obtaining vibronic excitation spectra of small organic molecules using machine learning simulations and power spectra techniques

Vibrationally-electronically excited spectra for small organic molecules are obtained from machine learning driven molecular dynamics trajectories in order to elucidate surface enhanced Raman spectroscopy (SERS) spectra. Vibronic excitation spectra can be obtained from molecular dynamics trajectories through autocorrelation and power spectrum based methods. Direct calculation of vibronic excitation spectra with computationally expensive wavefunction methods are usually impractical, and are often plagued by pragmatic considerations (such as the harmonic oscillator approximation or numerical differentiation techniques) which limit their applicability. Machine learning potentials offer a path toward the practical production of these through molecular dynamics trajectories. Using the ANI method for constructing machine learning potentials and the TRAVIS molecular analysis package for calculating trajectory autocorrelation, we produce vibronic excited state spectra while avoiding both the harmonic oscillator approximation and numerical differentiation. Suitable for higher temperature analysis and applicable across a wide range of electronic structure methods, this work demonstrates the predictive power machine learning models offer for vibronic excited state spectra in SERS phenomena.