Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

10 August 2021

Papers citing "Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential"

4 / 4 papers shown

Title
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 39 370 0 05 Aug 2022
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential Simon Axelrod E. Shakhnovich Rafael Gómez-Bombarelli 24 20 0 23 Jul 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 158 246 0 01 May 2021
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes Ryan-Rhys Griffiths Jake L. Greenfield Aditya R. Thawani Arian R. Jamasb Henry B. Moss Anthony Bourached Penelope Jones William McCorkindale Alexander A. Aldrick Matthew J. Fuchter Alpha A. Lee 14 13 0 28 Jun 2020