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Found 6 papers, 1 papers with code
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
ii) We investigate the transferability of our excited-state ML models in chemical space, i. e., whether an ML model can predict properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously.
Machine learning for electronically excited states of molecules
Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others.
Machine learning and excited-state molecular dynamics
Machine learning is employed at an increasing rate in the research field of quantum chemistry.
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings.
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
As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.
Machine learning enables long time scale molecular photodynamics simulations
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales.
