Machine learning enables long time scale molecular photodynamics
simulations

Machine learning enables long time scale molecular photodynamics simulations

22 November 2018

Julia Westermayr

Papers citing "Machine learning enables long time scale molecular photodynamics simulations"

9 / 9 papers shown

Title
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo Zeno Schätzle P. Szabó Alice Cuzzocrea Frank Noé 40 0 0 25 Mar 2025
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 59 4 0 12 Mar 2025
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments Viktor Zaverkin Julia Netz Fabian Zills Andreas Köhn Johannes Kastner AI4CE 32 16 0 03 Dec 2023
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields J. Frank Oliver T. Unke Klaus-Robert Muller Stefan Chmiela 35 3 0 21 Sep 2023
Lifelong Machine Learning Potentials Marco Eckhoff Markus Reiher 62 20 0 10 Mar 2023
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Tian Zheng Weihao Gao Chong-Jun Wang AI4CE 42 3 0 30 Nov 2021
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential Simon Axelrod E. Shakhnovich Rafael Gómez-Bombarelli 29 49 0 10 Aug 2021
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 34 888 0 14 Oct 2020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics Julia Westermayr M. Gastegger P. Marquetand AI4CE 8 130 0 17 Feb 2020