Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

17 February 2020

Papers citing "Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics"

3 / 3 papers shown

Title
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 66 6 0 12 Mar 2025
Neural networks and kernel ridge regression for excited states dynamics of CH $_2$ NH $_2^+$ : From single-state to multi-state representations and multi-property machine learning models Julia Westermayr Felix A Faber Anders S. Christensen O. von Lilienfeld P. Marquetand 22 40 0 18 Dec 2019
Machine learning enables long time scale molecular photodynamics simulations Julia Westermayr M. Gastegger M. Menger Sebastian Mai L. González Marquetand AI4CE 18 72 0 22 Nov 2018