Machine-learning-accelerated predictions of optical properties of condensed systems based on many-body perturbation theory*

Accurate and efficient predictions of absorption spectra of molecules and solids are essential for the understanding and rational design of broad classes of materials, including photo-absorbers in solar and photo-electrochemical cells and defective insulators and semiconductors hosting optically addressable spin-defects. We present an approach to improve the efficiency of first principles calculations of absorption spectra of complex materials at finite temperature, based on the solution of the Bethe-Salpeter equation (BSE) [1]. We use machine learning techniques to predict the spectra of snapshots extracted from ab initio molecular dynamics simulations, and we use data generated by explicitly solving the BSE for a small subset of snapshots. We present results for nanoclusters, solids, liquids, including water, and semiconductor-water interfaces.

[1] N. L. Nguyen, H. Ma, M. Govoni, F. Gygi, and G. Galli, Phys. Rev. Lett. 122 (2019).