Interpretable ML-DFT Framework for Performance Prediction and Structure–Activity Relationship Analysis of Acidic Copper Plating Levelers

High-performance levelers are crucial for microelectronic copper electroplating; yet, their development is hindered by costly experimental screening and unclear structure-activity relationships (SAR). This study proposes an integrated machine learning (ML) and density functional theory (DFT) framework for the rapid discovery of novel levelers. First, a data set was established using experimental Dissolution Peak Decrease Amount (DPDA) values from the literature and DFT-calculated adsorption energies (<i>E</i>_ads) as target values, with 24 theoretical calculation molecular properties serving as features. Predictive models, including XGBoost Regression (XGBR) and the Classification and Regression Tree (CART), were constructed and trained. Using these models, the DPDA and <i>E</i>_ads of 521 effective molecules─prescreened from a database of 29,785,186 compounds based on structural criteria─were predicted, leading to the identification of five superior novel levelers (Res-1 to Res-5). Pearson correlation and SHAP analyses identified the key descriptors governing leveling efficacy. Finally, multiangle theoretical evaluations confirmed that Res-5 significantly outperforms the industrial standard Janus Green B (JGB). Its exceptional performance originates from the electrophilic molecular backbone and stable weak interactions between N-N functional groups and the copper surface. This research not only provides a high-quality screening tool for high-performance levelers but also offers new insights into molecular SAR at complex electrochemical interfaces.