Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
Authors/Creators
- 1. Department of Chemistry, Southern Methodist University, PO Box 750314, Dallas, TX, USA
- 2. Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Str 38, Dresden, Germany
- 3. International Centre for Quantum and Molecular Structures and the School of Physics, Shanghai University, 200444, Shanghai, China and School of Mathematical and Physical Sciences, University of technology Sydney, NSW 2007, Australia
Description
(as README.txt):
Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.
Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.
Table of contents:
Figure_2:
- 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy.
- 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy.
- 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy.
Figure_3:
Panel A:
- Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.
- Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.
- Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.
- Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.
Panel B:
- Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.
- Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.
- Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.
Figure_4:
Panel_A:
- List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.
Panel_B:
- Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.
- Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.
Panel_C:
- Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.
- Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.
Panel_D:
- Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.
- Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.
- N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.
- N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.
Figure_5:
Panel_C:
- PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.
- PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.
Panel_D:
- PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.
- PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.
Figure_6:
Panel_A:
- PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.
- PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.
- PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.
Panel_B:
- PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.
- PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.
Panel_C:
- Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.
- Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.
- Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.
- Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.
- Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.
Panel_D:
- peak_00_position.npy: relative position of the 00 peak for different number of pigments.
- peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)
- peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.
- peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)
Figure_7:
- PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system.
- PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system.
- PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system.
The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages.