Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics
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074111_1_online.pdf
Description
Published version
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3.54 MB
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Author(s)
Farahvash, Ardavan
Lee, Chee-Kong
Sun, Qiming
Shi, Liang
Willard, Adam P
Date Issued
August 20, 2020
Journal
The Journal of Chemical Physics
Publisher
AIP Publishing
Citation
Ardavan Farahvash, Chee-Kong Lee, Qiming Sun, Liang Shi, Adam P. Willard; Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics. J. Chem. Phys. 21 August 2020; 153 (7): 074111.
Version
Final published version
Abstract
In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. Here, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1063/5.0016009
