Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics. - Julia Westermayr; Michael Gastegger; Philipp Marquetand (CoRR, 2020)

Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models. - Julia Westermayr; Felix A. Faber; Anders S. Christensen; O. Anatole von Lilienfeld; Philipp Marquetand (Mach. Learn. Sci. Technol., 2020)

Machine learning and excited-state molecular dynamics. - Julia Westermayr; Philipp Marquetand (Mach. Learn. Sci. Technol., 2020)

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? - Leon Gerard; Michael Scherbela; Philipp Marquetand; Philipp Grohs (NeurIPS, 2022)

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. - Michael Scherbela; Rafael Reisenhofer; Leon Gerard; Philipp Marquetand; Philipp Grohs (Nat. Comput. Sci., 2022)

Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials. - Maximilian X. Tiefenbacher; Brigitta Bachmair; Cheng Giuseppe Chen; Julia Westermayr; Philipp Marquetand; Johannes C. B. Dietschreit; Leticia González (CoRR, 2025)