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