4.8 Article

From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning

期刊

ACS NANO
卷 14, 期 6, 页码 6589-6598

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c00384

关键词

machine learning; bayesian models; PEDOT; multiscale computational workflow; ab initio calculations; molecular dynamics; tight-binding formalism

资金

  1. Health Sciences Platform (HSP) at Tianjin Univ., P. R. China
  2. FAS Division of Science, Research Computing Group at Harvard University, United States
  3. Ministry of Science, Research and the Arts Baden-Wurttemberg
  4. Deutsche Forschungsgemeinschaft (DFG)
  5. Natural Resources Canada [EIP2-MAT-001]
  6. Herchel Smith Graduate Fellowship
  7. Jacques-Emile Dubois Student Dissertation Fellowship
  8. European Unions [795206]
  9. National Science Foundation [NSF: DMR-1610345]
  10. Real Colegio Complutense in Harvard
  11. Spanish Ministerio de Ciencia e Innovacion through the Salvador de Madariaga Program
  12. Marie Curie Actions (MSCA) [795206] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.

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