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

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

Julia Westermayr
M. Gastegger
P. Marquetand
ArXivPDFHTML

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
Neural networks and kernel ridge regression for excited states dynamics of CH2_2NH2+_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
Machine learning enables long time scale molecular photodynamics simulations
Julia Westermayr
M. Gastegger
M. Menger
Sebastian Mai
L. González
Marquetand
AI4CE
16
72
0
22 Nov 2018

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.