Nuclear quantum effects slow down the energy transfer in biological light-harvesting complexes

Published May 15, 2025 on Dryad. https://doi.org/10.5061/dryad.sj3tx96fn

Data files

May 15, 2025 version files 7.39 MB

README.md
4.56 KB
results.zip
7.38 MB

Abstract

We assess how quantum-mechanical effects associated with high-frequency chromophore vibrations influence excitation energy transfer in biological light-harvesting complexes. After defining a classical nuclear limit that is consistent with the quantum-classical equilibrium, we include nuclear quantum effects through a variational polaron transformation of the high-frequency vibrational modes. This approach is validated by comparison with fully quantum-mechanical benchmark calculations and applied to three prototypical light-harvesting complexes. For light-harvesting complex 2 of purple bacteria, the inter-ring transfer is 1.5 times slower in the quantum treatment than the classical. For the Fenna-Matthews-Olson complex, the transfer rate is the same in both cases, whereas for light-harvesting complex II of spinach, transfer is 1.7 times slower in the quantum treatment. The effect is most pronounced for systems with large excitonic energy gaps and strong vibronic coupling to high-frequency modes. In all cases, nuclear quantum effects are found to be unimportant for the directionality of energy transfer.

https://doi.org/10.5061/dryad.sj3tx96fn

Description of the data and file structure

Files and variables

File: results.zip

Description:

Files starting with “LH2”, “FMO” or “LHC2” contains the plotted data with time (in fs) as first column and populations (unitless) in the following columns.

Each file is named according to the pattern [system]-[population type or figure number]-[method].dat

[system]:
- LH2: 24-site light-harvesting complex 2 in purple bacteria
- FMO: 7-site Fenna-Matthews-Olson complex
- LHC2: 14-site light-harvesting couplex II in spinach
[population type or figure number]:
- B850: total population of sites 16-24
- sites: population of each site 1-24 in the columns following time
- Pa: total population of Chla sites
- fig2: population of each site 1-7 for the model used in Fig2
- figS1: population of each site 1-7 for the model used in FigS1
[method]:
- all_classical: MASH with all modes classical
- VPT: MASH with the variational polaron transformation for the high frequencies
- without_high: MASH without high frequencies
- wigner: MASH with an initial Wigner distribution
- without_any_discrete: MASH without any discrete modes

Files starting with “SI” contain the equilibrium site populations plotted in figure S2. The first column is the mean population, and the second column is the standard error in the mean. The row number corresponds to the site index. Here, the [method] refers to

bare: equilibrium of the uncoupled system
classical: single bead equilibrium
quantum: 8-bead equilibrium

Overview

This directory contains

A main Python script mash.py
A script model.py with functions for setting up the model, handling input/output, sampling initial variables, and running a debug trajectory
A Fortran source code in src/ that runs the trajectories
These allow you to run MASH for various potentials, initial conditions and observables.

How to compile

Prerequisites:

gfortran (it also compiles and runs with ifort, but you will need to manually update the makefile)
lapack
numpy.f2py for Python3

Compile by typing make [option] and one of the following options

clean Remove compiled files and restart from clean directory
fast Produces fast parallelized code
debug Uses plenty of warning flags and should tell at which line the code breaks.

Also check that you have the required Python packages installed.
One way to ensure this is to (preferably in a virtual environment) run pip install -r requirements.txt.

How to run

mash.py +[args].in
where [args].in is an argument file with one line per argument (allowing commented lines). See examples for example input files.
There are plenty of available option flags and you can add more to suit your system.

Hints:

Make sure mash.py is executable, otherwise chmod u+x mash.py
Create a symbolic link to mash.py in some place in your PATH, so that you can call it from anywhere.
You may need to add the directory to your PYTHONPATH.

Input

Run mash.py -h to see the available options. In general you should know about the following:

model String specifying your model system.
Model-specific parameters like beta, Delta etc.
init Integer that specifies initial state (index starts at 0)
dt Timestep
nt Number of timesteps
ntraj Number of trajectories
nucsamp Nuclear sampling option (see “sampling” in model.py)
elsamp Electronic sampling option (see “sampling” in model.py)
obstyp Specify what kind of observables you want to measure (e.g. pop for populations)

Output

You should see a file pop.out which contains time in the first column and then the state populations in the following columns.

Examples

The examples folder contains lh2.in, fmo8.in, and lhc2.in with input parameters appropriate for the corresponding models in the associated paper. (Note: these are example inputs only – the actual convergence parameters are indicated in the paper.)

Access information

Other publicly accessible locations of the data:

Data was derived from the following sources:

The files LH2-B850-fully_quantum.dat and LH2-B850-no_backaction (included for comparison) were obtained by screen-grabbing from Kundu, Dani and Makri, Sci. Adv., 8, eadd0023 (2022).