Machine Learning Exciton Dynamics

Abstract

Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine learning technique, multi-layer perceptrons, as a tool to reduce the time required to compute excited state energies. With this approach we predict time-dependent density functional theory (TDDFT) excited state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO) complex. Additionally we compute spectral densities and exciton populations from the predictions. Different methods to determine multi-layer perceptron training sets are introduced, leading to several initial data selections. In addition, we compute spectral densities and exciton populations. Once multi-layer perceptrons are trained, predicting excited state energies was found to be significantly faster than the corresponding QM/MM calculations. We showed that multi-layer perceptrons can successfully reproduce the energies of QM/MM calculations to a high degree of accuracy with prediction errors contained within 0.01 eV (0.5%). Spectral densities and exciton dynamics are also in agreement with the TDDFT results. The acceleration and accurate prediction of dynamics strongly encourage the combination of machine learning techniques with ab-initio methods.

References in corpus (10)

• Environment-Assisted Quantum Walks in Photosynthetic Energy Transfer
• Dephasing assisted transport: Quantum networks and biomolecules
• Environment-Assisted Quantum Transport
• Electronic Spectra from TDDFT and Machine Learning in Chemical Space
• High-performance solution of hierarchical equations of motions for studying energy-transfer in light-harvesting complexes
• On the alternatives for bath correlators and spectral densities from mixed quantum-classical simulations
• Modified-scaled hierarchical equation of motion approach for the study of quantum coherence in photosynthetic complexes
• Influence of Complex Exciton-Phonon Coupling on Optical Absorption and Energy Transfer of Quantum Aggregates
• Computational Methodologies and Physical Insights into Electronic Energy Transfer in Photosynthetic Light-Harvesting Complexes
• Exciton-phonon information flow in the energy transfer process of photosynthetic complexes

Cited by in corpus (28)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Machine learning for electronically excited states of molecules
• Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Applying machine learning techniques to predict the properties of energetic materials
• Machine learning enables long time scale molecular photodynamics simulations
• Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
• Perspective on integrating machine learning into computational chemistry and materials science
• Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks
• Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Quantum Machine Learning for Electronic Structure Calculations
• Neural networks and kernel ridge regression for excited states dynamics of CH${}_2$NH${}_2^{+}$: From single-state to multi-state representations and multi-property machine learning models
• Machine learning and excited-state molecular dynamics
• Machine Learning Quantum Reaction Rate Constants
• Accelerating Atomistic Simulations with Piecewise Machine Learned Ab Initio Potentials at Classical Force Field-like Cost
• Machine Learning Prediction of DNA Charge Transport
• Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks
• Multi-Fidelity Machine Learning for Excited State Energies of Molecules
• Predicting excited states from ground state wavefunction by supervised quantum machine learning
• Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
• Machine learning for excitation energy transfer dynamics
• Global Structure Search for Molecules on Surfaces: Efficient Sampling with Curvilinear Coordinates
• MLQD: A package for machine learning-based quantum dissipative dynamics
• Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
• Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations
• Theory of Moment Propagation for Quantum Dynamics in Single-Particle Description