PMID- 29619189
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240321
IS  - 2041-6520 (Print)
IS  - 2041-6539 (Electronic)
IS  - 2041-6520 (Linking)
VI  - 8
IP  - 12
DP  - 2017 Dec 1
TI  - Machine learning for quantum dynamics: deep learning of excitation energy 
      transfer properties.
PG  - 8419-8426
LID - 10.1039/c7sc03542j [doi]
AB  - 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 propose 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 exhibit similar or even higher 
      accuracies than frequently used approximate methods such as secular Redfield 
      theory.
FAU - Hase, Florian
AU  - Hase F
AD  - Department of Chemistry and Chemical Biology , Harvard University , Cambridge , 
      02138 , USA . Email: christophkreisbeck@gmail.com ; Email: 
      aspuru@chemistry.harvard.edu ; Tel: +1-617-384-8188.
FAU - Kreisbeck, Christoph
AU  - Kreisbeck C
AD  - Department of Chemistry and Chemical Biology , Harvard University , Cambridge , 
      02138 , USA . Email: christophkreisbeck@gmail.com ; Email: 
      aspuru@chemistry.harvard.edu ; Tel: +1-617-384-8188.
FAU - Aspuru-Guzik, Alan
AU  - Aspuru-Guzik A
AUID- ORCID: 0000-0003-3711-9761
AD  - Department of Chemistry and Chemical Biology , Harvard University , Cambridge , 
      02138 , USA . Email: christophkreisbeck@gmail.com ; Email: 
      aspuru@chemistry.harvard.edu ; Tel: +1-617-384-8188.
LA  - eng
PT  - Journal Article
DEP - 20171023
PL  - England
TA  - Chem Sci
JT  - Chemical science
JID - 101545951
PMC - PMC5863613
EDAT- 2018/04/06 06:00
MHDA- 2018/04/06 06:01
PMCR- 2017/12/01
CRDT- 2018/04/06 06:00
PHST- 2017/08/13 00:00 [received]
PHST- 2017/10/23 00:00 [accepted]
PHST- 2018/04/06 06:00 [entrez]
PHST- 2018/04/06 06:00 [pubmed]
PHST- 2018/04/06 06:01 [medline]
PHST- 2017/12/01 00:00 [pmc-release]
AID - c7sc03542j [pii]
AID - 10.1039/c7sc03542j [doi]
PST - ppublish
SO  - Chem Sci. 2017 Dec 1;8(12):8419-8426. doi: 10.1039/c7sc03542j. Epub 2017 Oct 23.