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Title: Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

Abstract

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment–protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We present the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies demonstrate similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.

Authors:
 [1];  [1]; ORCiD logo [1]
  1. Harvard Univ., Cambridge, MA (United States)
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1409485
Alternate Identifier(s):
OSTI ID: 1506094
Grant/Contract Number:  
SC0001088
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Volume: 8; Journal Issue: 12; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Häse, Florian, Kreisbeck, Christoph, and Aspuru-Guzik, Alán. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. United States: N. p., 2017. Web. doi:10.1039/c7sc03542j.
Häse, Florian, Kreisbeck, Christoph, & Aspuru-Guzik, Alán. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. United States. doi:10.1039/c7sc03542j.
Häse, Florian, Kreisbeck, Christoph, and Aspuru-Guzik, Alán. Mon . "Machine learning for quantum dynamics: deep learning of excitation energy transfer properties". United States. doi:10.1039/c7sc03542j.
@article{osti_1409485,
title = {Machine learning for quantum dynamics: deep learning of excitation energy transfer properties},
author = {Häse, Florian and Kreisbeck, Christoph and Aspuru-Guzik, Alán},
abstractNote = {Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment–protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We present the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies demonstrate similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.},
doi = {10.1039/c7sc03542j},
journal = {Chemical Science},
number = 12,
volume = 8,
place = {United States},
year = {2017},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1039/c7sc03542j

Citation Metrics:
Cited by: 12 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: Machine learning excitation energy transfer properties in open quantum systems. (A) Fenna–Matthews–Olson (FMO) pigment–protein complex with eight chlorophyll pigments in the conventional numbering scheme. Dominant energy transfer pathways from the donor pigment 8 (blue) to the acceptor pigment 3 (orange) are indicated. (B) Results for average transfer timemore » $\langle$t$\rangle$ calculations for energy transfer in the FMO complex from the donor to the acceptor obtained from solving the hierarchical equations of motion (HEOM), the approximate secular Redfield formalism and predicted by multi-layer perceptrons (MLPs) designed in this study. Computational costs are reported for each method. (C) Illustration of the MLP architecture. MLPs accept Frenkel exciton Hamiltonians as input feature and predict average transfer times and efficiencies. The best network architectures were obtained through Bayesian optimization.« less

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Works referenced in this record:

Coherently wired light-harvesting in photosynthetic marine algae at ambient temperature
journal, February 2010

  • Collini, Elisabetta; Wong, Cathy Y.; Wilk, Krystyna E.
  • Nature, Vol. 463, Issue 7281
  • DOI: 10.1038/nature08811

Spectroscopic Properties of Reaction Center Pigments in Photosystem II Core Complexes: Revision of the Multimer Model
journal, July 2008


Absence of Selection for Quantum Coherence in the Fenna–Matthews–Olson Complex: A Combined Evolutionary and Excitonic Study
journal, August 2017


Using coherence to enhance function in chemical and biophysical systems
journal, March 2017

  • Scholes, Gregory D.; Fleming, Graham R.; Chen, Lin X.
  • Nature, Vol. 543, Issue 7647
  • DOI: 10.1038/nature21425

Structure-Dynamics Relation in Physically-Plausible Multi-Chromophore Systems
journal, May 2017

  • Knee, George C.; Rowe, Patrick; Smith, Luke D.
  • The Journal of Physical Chemistry Letters, Vol. 8, Issue 10
  • DOI: 10.1021/acs.jpclett.7b00829

Efficiency of energy funneling in the photosystem II supercomplex of higher plants
journal, January 2016

  • Kreisbeck, Christoph; Aspuru-Guzik, Alán
  • Chemical Science, Vol. 7, Issue 7
  • DOI: 10.1039/C5SC04296H

Hierarchical approach based on stochastic decoupling to dissipative systems
journal, September 2004


Optimal networks for excitonic energy transport
journal, September 2011

  • Scholak, Torsten; Wellens, Thomas; Buchleitner, Andreas
  • Journal of Physics B: Atomic, Molecular and Optical Physics, Vol. 44, Issue 18
  • DOI: 10.1088/0953-4075/44/18/184012

Unified treatment of quantum coherent and incoherent hopping dynamics in electronic energy transfer: Reduced hierarchy equation approach
journal, June 2009

  • Ishizaki, Akihito; Fleming, Graham R.
  • The Journal of Chemical Physics, Vol. 130, Issue 23
  • DOI: 10.1063/1.3155372

Coherent versus Incoherent Energy Transfer and Trapping in Photosynthetic Antenna Complexes
journal, January 1996

  • Leegwater, Jan A.
  • The Journal of Physical Chemistry, Vol. 100, Issue 34
  • DOI: 10.1021/jp961448i

Kinetics and mechanism of electron transfer in intact photosystem II and in the isolated reaction center: Pheophytin is the primary electron acceptor
journal, April 2006

  • Holzwarth, A. R.; Muller, M. G.; Reus, M.
  • Proceedings of the National Academy of Sciences, Vol. 103, Issue 18
  • DOI: 10.1073/pnas.0505371103

How Proteins Trigger Excitation Energy Transfer in the FMO Complex of Green Sulfur Bacteria
journal, October 2006


Explicit Correlated Exciton-Vibrational Dynamics of the FMO Complex
journal, May 2015


Machine learning of molecular electronic properties in chemical compound space
journal, September 2013


Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems
journal, April 2007

  • Engel, Gregory S.; Calhoun, Tessa R.; Read, Elizabeth L.
  • Nature, Vol. 446, Issue 7137
  • DOI: 10.1038/nature05678

Environment-assisted quantum walks in photosynthetic energy transfer
journal, November 2008

  • Mohseni, Masoud; Rebentrost, Patrick; Lloyd, Seth
  • The Journal of Chemical Physics, Vol. 129, Issue 17
  • DOI: 10.1063/1.3002335

Long-Lived Electronic Coherence in Dissipative Exciton Dynamics of Light-Harvesting Complexes
journal, September 2012

  • Kreisbeck, Christoph; Kramer, Tobias
  • The Journal of Physical Chemistry Letters, Vol. 3, Issue 19
  • DOI: 10.1021/jz3012029

Structure-based simulation of linear optical spectra of the CP43 core antenna of photosystem II
journal, August 2011

  • Müh, Frank; Madjet, Mohamed El-Amine; Renger, Thomas
  • Photosynthesis Research, Vol. 111, Issue 1-2
  • DOI: 10.1007/s11120-011-9675-8

Time Evolution of a Quantum System in Contact with a Nearly Gaussian-Markoffian Noise Bath
journal, January 1989

  • Tanimura, Yoshitaka; Kubo, Ryogo
  • Journal of the Physical Society of Japan, Vol. 58, Issue 1
  • DOI: 10.1143/JPSJ.58.101

Multiscale model of light harvesting by photosystem II in plants
journal, January 2016

  • Amarnath, Kapil; Bennett, Doran I. G.; Schneider, Anna R.
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 5
  • DOI: 10.1073/pnas.1524999113

Distribution of entanglement in light-harvesting complexes and their quantum efficiency
journal, August 2010


Machine learning exciton dynamics
journal, January 2016

  • Häse, Florian; Valleau, Stéphanie; Pyzer-Knapp, Edward
  • Chemical Science, Vol. 7, Issue 8
  • DOI: 10.1039/C5SC04786B

Distinguishing the roles of energy funnelling and delocalization in photosynthetic light harvesting
journal, January 2016

  • Baghbanzadeh, Sima; Kassal, Ivan
  • Physical Chemistry Chemical Physics, Vol. 18, Issue 10
  • DOI: 10.1039/C6CP00104A

Light Harvesting in Photosystem II Core Complexes Is Limited by the Transfer to the Trap:  Can the Core Complex Turn into a Photoprotective Mode?
journal, April 2008

  • Raszewski, Grzegorz; Renger, Thomas
  • Journal of the American Chemical Society, Vol. 130, Issue 13
  • DOI: 10.1021/ja7099826

Scalable High-Performance Algorithm for the Simulation of Exciton Dynamics. Application to the Light-Harvesting Complex II in the Presence of Resonant Vibrational Modes
journal, August 2014

  • Kreisbeck, Christoph; Kramer, Tobias; Aspuru-Guzik, Alán
  • Journal of Chemical Theory and Computation, Vol. 10, Issue 9
  • DOI: 10.1021/ct500629s

Two-dimensional spectroscopy of electronic couplings in photosynthesis
journal, March 2005

  • Brixner, Tobias; Stenger, Jens; Vaswani, Harsha M.
  • Nature, Vol. 434, Issue 7033
  • DOI: 10.1038/nature03429

Exact quantum master equation via the calculus on path integrals
journal, January 2005

  • Xu, Rui-Xue; Cui, Ping; Li, Xin-Qi
  • The Journal of Chemical Physics, Vol. 122, Issue 4
  • DOI: 10.1063/1.1850899

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
journal, August 2011

  • Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule
  • The Journal of Physical Chemistry Letters, Vol. 2, Issue 17
  • DOI: 10.1021/jz200866s

Hierarchy of Stochastic Pure States for Open Quantum System Dynamics
journal, October 2014


Visualizing charge separation in bulk heterojunction organic solar cells
journal, August 2013

  • Vithanage, D. Amarasinghe; Devižis, A.; Abramavičius, V.
  • Nature Communications, Vol. 4, Issue 1
  • DOI: 10.1038/ncomms3334

Quantum coherence in photosynthesis for efficient solar-energy conversion
journal, July 2014

  • Romero, Elisabet; Augulis, Ramunas; Novoderezhkin, Vladimir I.
  • Nature Physics, Vol. 10, Issue 9
  • DOI: 10.1038/nphys3017

Tracking the coherent generation of polaron pairs in conjugated polymers
journal, December 2016

  • De Sio, Antonietta; Troiani, Filippo; Maiuri, Margherita
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms13742

Structure–dynamics relationship in coherent transport through disordered systems
journal, August 2013

  • Mostarda, Stefano; Levi, Federico; Prada-Gracia, Diego
  • Nature Communications, Vol. 4, Issue 1
  • DOI: 10.1038/ncomms3296

The many-body expansion combined with neural networks
journal, January 2017

  • Yao, Kun; Herr, John E.; Parkhill, John
  • The Journal of Chemical Physics, Vol. 146, Issue 1
  • DOI: 10.1063/1.4973380

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007


Exciton transfer dynamics and quantumness of energy transfer in the Fenna-Matthews-Olson complex
journal, October 2011


Quantum Dynamics of System Strongly Coupled to Low-Temperature Colored Noise Bath: Reduced Hierarchy Equations Approach
journal, December 2005

  • Ishizaki, Akihito; Tanimura, Yoshitaka
  • Journal of the Physical Society of Japan, Vol. 74, Issue 12
  • DOI: 10.1143/JPSJ.74.3131

Modelling of oscillations in two-dimensional echo-spectra of the Fenna–Matthews–Olson complex
journal, February 2012


Highly efficient energy excitation transfer in light-harvesting complexes: The fundamental role of noise-assisted transport
journal, January 2009

  • Caruso, F.; Chin, A. W.; Datta, A.
  • The Journal of Chemical Physics, Vol. 131, Issue 10
  • DOI: 10.1063/1.3223548

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
journal, July 2013

  • Hansen, Katja; Montavon, Grégoire; Biegler, Franziska
  • Journal of Chemical Theory and Computation, Vol. 9, Issue 8
  • DOI: 10.1021/ct400195d

Hot charge-transfer excitons set the time limit for charge separation at donor/acceptor interfaces in organic photovoltaics
journal, December 2012

  • Jailaubekov, Askat E.; Willard, Adam P.; Tritsch, John R.
  • Nature Materials, Vol. 12, Issue 1
  • DOI: 10.1038/nmat3500

Dynamics of Light Harvesting in Photosynthesis
journal, May 2009


Geometry, Supertransfer, and Optimality in the Light Harvesting of Purple Bacteria
journal, September 2016


Environment-assisted quantum transport
journal, March 2009


Chlorophyll arrangement in a bacteriochlorophyll protein from Chlorobium limicola
journal, December 1975

  • Fenna, R. E.; Matthews, B. W.
  • Nature, Vol. 258, Issue 5536
  • DOI: 10.1038/258573a0

Vibronic Enhancement of Algae Light Harvesting
journal, December 2016


The role of non-equilibrium vibrational structures in electronic coherence and recoherence in pigment–protein complexes
journal, January 2013

  • Chin, A. W.; Prior, J.; Rosenbach, R.
  • Nature Physics, Vol. 9, Issue 2
  • DOI: 10.1038/nphys2515

Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo
journal, June 2008


Lessons from nature about solar light harvesting
journal, September 2011

  • Scholes, Gregory D.; Fleming, Graham R.; Olaya-Castro, Alexandra
  • Nature Chemistry, Vol. 3, Issue 10
  • DOI: 10.1038/nchem.1145

To address surface reaction network complexity using scaling relations machine learning and DFT calculations
journal, March 2017

  • Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms14621

High-Performance Solution of Hierarchical Equations of Motion for Studying Energy Transfer in Light-Harvesting Complexes
journal, June 2011

  • Kreisbeck, Christoph; Kramer, Tobias; Rodríguez, Mirta
  • Journal of Chemical Theory and Computation, Vol. 7, Issue 7
  • DOI: 10.1021/ct200126d

Functional architecture of higher plant photosystem II supercomplexes
journal, August 2009

  • Caffarri, Stefano; Kouřil, Roman; Kereïche, Sami
  • The EMBO Journal, Vol. 28, Issue 19
  • DOI: 10.1038/emboj.2009.232

Origin of Long-Lived Coherences in Light-Harvesting Complexes
journal, June 2012

  • Christensson, Niklas; Kauffmann, Harald F.; Pullerits, Tõnu
  • The Journal of Physical Chemistry B, Vol. 116, Issue 25
  • DOI: 10.1021/jp304649c

    Works referencing / citing this record:

    Challenges and opportunities of polymer design with machine learning and high throughput experimentation
    journal, May 2019

    • Kumar, Jatin N.; Li, Qianxiao; Jun, Ye
    • MRS Communications, Vol. 9, Issue 02
    • DOI: 10.1557/mrc.2019.54

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