Machine learning enables long time scale molecular photodynamics
simulations

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Machine learning enables long time scale molecular photodynamics simulations

22 November 2018

Julia Westermayr

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

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Title
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo Zeno Schätzle P. Szabó Alice Cuzzocrea Frank Noé 109 1 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 113 9 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 49 18 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 69 3 0 21 Sep 2023
Lifelong Machine Learning Potentials Marco Eckhoff Markus Reiher 113 24 0 10 Mar 2023
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Tian Zheng Weihao Gao Chong-Jun Wang AI4CE 68 4 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 101 53 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 143 940 0 14 Oct 2020
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics Julia Westermayr M. Gastegger P. Marquetand AI4CE 67 131 0 17 Feb 2020