A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
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Detection operator in 2D electronic spectroscopy operationally defines dephasing, with coherent emission retaining standard T2 connection while population observables encode population redistribution leading to distinct apparent linewidths.
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Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
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Detection Defines Dephasing in Two-Dimensional Electronic Spectroscopy of Materials: Coherent Field Emission versus Incoherent Population Observables
Detection operator in 2D electronic spectroscopy operationally defines dephasing, with coherent emission retaining standard T2 connection while population observables encode population redistribution leading to distinct apparent linewidths.