Machine learning to accelerate screening for Marcus reorganization energies

Abarbanel, Omri D.; Hutchison, Geoffrey R.

doi:10.1063/5.0059682

Title: Machine learning to accelerate screening for Marcus reorganization energies

Journal Article · 02 August 2021 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/5.0059682 · OSTI ID:1853100

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University of Pittsburgh, PA (United States); Univ. of Pittsburgh, PA (United States)
University of Pittsburgh, PA (United States)

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

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Research Organization:: University of Pittsburgh, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019335

OSTI ID:: 1853100

Journal Information:: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 5 Vol. 155; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine learning to accelerate screening for Marcus reorganization energies

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