Machine learning for excitation energy transfer dynamics

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.

9 pages, 4 figures

References in corpus (2)

• Highly efficient energy excitation transfer in light-harvesting complexes: The fundamental role of noise-assisted transport
• Quantum transfer through small networks coupled to phonons: effects of topology vs phonons

Cited by in corpus (5)

• MLQD: A package for machine learning-based quantum dissipative dynamics
• QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets
• Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices
• Multi-timescale time encoding for CNN prediction of Fenna-Matthews-Olson energy-transfer dynamics
• Predicting quantum evolutions of excitation energy transfer in a light-harvesting complex using multi-optimized recurrent neural networks