Machine learning for excitation energy transfer dynamics
- URL: http://arxiv.org/abs/2112.11889v1
- Date: Wed, 22 Dec 2021 14:11:30 GMT
- Title: Machine learning for excitation energy transfer dynamics
- Authors: Kimara Naicker, Ilya Sinayskiy, Francesco Petruccione
- Abstract summary: We use the hierarchical equations of motion (HEOM) to simulate open quantum dynamics in the biological regime.
We generate a set of time dependent observables that depict the coherent propagation of electronic excitations through the light harvesting complexes.
We demonstrate the capability of convolutional neural networks to tackle this research problem.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A well-known approach to describe the dynamics of an open quantum system is
to compute the master equation evolving the reduced density matrix of the
system. This approach plays an important role in describing excitation transfer
through photosynthetic light harvesting complexes (LHCs). The hierarchical
equations of motion (HEOM) was adapted by Ishizaki and Fleming (J. Chem. Phys.,
2009) to simulate open quantum dynamics in the biological regime. We generate a
set of time dependent observables that depict the coherent propagation of
electronic excitations through the LHCs by solving the HEOM. We solve the
inverse problem using classical machine learning (ML) models as this is a
computationally intractable problem. The objective here is to determine whether
a trained ML model can perform Hamiltonian tomography by using the time
dependence of the observables as inputs. We demonstrate the capability of
convolutional neural networks to tackle this research problem. The models
developed here can predict Hamiltonian parameters such as excited state
energies and inter-site couplings of a system up to 99.28\% accuracy and
mean-squared error as low as 0.65.
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