Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model

Ren, Fangning; Chen, Xu; Liu, Fang

doi:10.1021/acs.jpclett.4c03548

Title: Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model

Journal Article · 02 March 2025 · Journal of Physical Chemistry Letters

DOI: https://doi.org/10.1021/acs.jpclett.4c03548 · OSTI ID:2526293

^[1]; Chen, Xu ^[1];

^[1]

Emory University, Atlanta, GA (United States)

Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.

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Research Organization:: Emory University, Atlanta, GA (United States)

Sponsoring Organization:: American Chemical Society Petroleum Research; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0025345

OSTI ID:: 2526293

Journal Information:: Journal of Physical Chemistry Letters, Journal Name: Journal of Physical Chemistry Letters Journal Issue: 10 Vol. 16; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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