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)

High-throughput property-driven generative design of functional organic molecules. - Julia Westermayr; Joe Gilkes; Rhyan Barrett; Reinhard Johann Maurer (Nat. Comput. Sci., 2023)

Parameterizing Intersecting Surfaces via Invariants. - Timon S. Gutleb; Rhyan Barrett; Julia Westermayr; Christoph Ortner (CoRR, 2024)

Decoding energy decomposition analysis: Machine-learned Insights on the impact of the density functional on the bonding analysis. - Toni Oestereich; Ralf Tonner-Zech; Julia Westermayr (J. Comput. Chem., 2024)

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)

Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports. - Sophia Wesely; Ella Hofer; Robin Curth; Shyam Paryani; Nicole Mills; Olaf Ueberschär; Julia Westermayr (CoRR, 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)