fig8

Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning

Figure 8. Comparison of the calculated DFT values for (A) Hole reorganization energy, (B) Solvation free energy, (C) Maximum absorption, and (D) LogP of common HTMs with the predicted values from RF, GBDT and XGBoost ML models, respectively. DFT: Density functional theory; HTMs: hole transport materials; RF: random forest; GBDT: gradient boosted decision tree; XGBoost: extreme gradient boosting; ML: machine learning.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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