研究目的
To investigate structure–property relationships and develop quantitative structure–property relationship (QSPR) models for predicting the intramolecular reorganization energy (RE) of p-type organic semiconductors from molecular structure, facilitating large-scale computational screening.
研究成果
The study successfully developed QSPR models for predicting reorganization energy in p-type organic semiconductors, with the best performance achieved using partial least squares regression with molecular signature descriptors (R2 up to 0.7, MAE of 40 meV). The findings highlight the importance of a diverse and larger training set for improved predictive accuracy and generalizability. Future work should focus on expanding the molecular library and exploring higher-level descriptors.
研究不足
The library size of 171 molecules is relatively small, leading to potential overfitting and limited generalizability of models. The RE calculations are based on gas-phase intramolecular contributions only, neglecting intermolecular effects. The use of B3LYP functional may introduce inaccuracies, and the models require ground-state neutral descriptors, which may not fully capture charging processes.
1：Experimental Design and Method Selection:
The study employs QSPR methodology using regression models (multiple linear regression, partial least squares, principal component regression) with molecular descriptors derived from SMILES strings and 3D geometries. Density functional theory (DFT) calculations at B3LYP/6-31G(d,p) level were used to compute RE values for a library of 171 molecules.
2：Sample Selection and Data Sources:
A compound set of 171 p-type organic semiconductors, including acenes, thiophenes, thienoacenes, and pentalenes, was generated from experimentally known molecules and computational screening studies.
3：List of Experimental Equipment and Materials:
Computational software packages including Q-Chem for DFT calculations, ChemAxon tools (molconvert, cxcalc) for molecular representations, and Python libraries (statsmodels, scikit-learn) for data analysis.
4：Experimental Procedures and Operational Workflow:
Molecules were represented as SMILES strings and converted to descriptor vectors (structural, electronic, signature, molecular transform). DFT optimizations and frequency analyses were performed to ensure minima. Regression models were trained and validated using 5-fold cross-validation with random shuffling.
5：Data Analysis Methods:
Statistical analysis included Pearson's correlation, multiple linear regression, principal component analysis, partial least squares regression, and calculation of coefficients of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE).
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