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Title: Machine learning exciton dynamics

Journal Article · · Chemical Science
DOI:https://doi.org/10.1039/c5sc04786b· OSTI ID:1387693
 [1]; ORCiD logo [2];  [2]; ORCiD logo [2]
  1. Harvard Univ., Cambridge, MA (United States); Technische Univ. Munchen, Garching (Germany)
  2. Harvard Univ., Cambridge, MA (United States)
+ Show Author Affiliations

Machine learning ground state QM/MM for accelerated computation of exciton dynamics.

Research Organization:
Energy Frontier Research Centers (EFRC) (United States). Center for Excitonics (CE)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0001088
OSTI ID:
1387693
Journal Information:
Chemical Science, Vol. 7, Issue 8; Related Information: CE partners with Massachusetts Institute of Technology (lead); Brookhaven National Laboratory; Harvard University; ISSN 2041-6520
Publisher:
Royal Society of ChemistryCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 102 works
Citation information provided by
Web of Science

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Cited By (20)

Nonadiabatic dynamics: The SHARC approach
  • Mai, Sebastian; Marquetand, Philipp; González, Leticia
  • Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 8, Issue 6 https://doi.org/10.1002/wcms.1370
journal May 2018
Quantum machine learning for electronic structure calculations journal October 2018
Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models journal August 2017
Applying machine learning techniques to predict the properties of energetic materials journal June 2018
Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches journal January 2017
Predicting electronic structure properties of transition metal complexes with neural networks journal January 2017
How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry journal January 2019
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels journal June 2017
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials journal June 2018
A neural network protocol for electronic excitations of N -methylacetamide journal May 2019
From DFT to machine learning: recent approaches to materials science–a review journal May 2019
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery journal November 2019
Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules journal January 2020
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost text January 2017
Applying Machine Learning Techniques to Predict the Properties of Energetic Materials posted_content February 2018
Applying Machine Learning Techniques to Predict the Properties of Energetic Materials posted_content February 2018
Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks text January 2017
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials text January 2017
Machine learning enables long time scale molecular photodynamics simulations text January 2018
Machine Learning Prediction of DNA Charge Transport text January 2018

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Related Subjects

97 MATHEMATICS AND COMPUTING
solar (photovoltaic)
solid state lighting
photosynthesis (natural and artificial)
charge transport
optics
synthesis (novel materials)
synthesis (self-assembly)
synthesis (scalable processing)