用可解释模型设计硼基路易斯酸，精准预测酸性并指导分子改造。
Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids
- 基于化学有意义特征构建可解释模型，融合量子计算与哈梅特参数。
- 预测误差低于6 kJ/mol，低数据下优于黑箱深度学习模型。
- 揭示取代基对酸性的调控机制，适合化学合成与材料设计者参考。
机器学习在化学中具有变革潜力，但常忽视可解释性。本研究利用可解释AI探索硼基路易斯酸的理性设计，以氟离子亲和力作为路易斯酸性的代理指标。通过结合从头算计算特征与基于哈梅特线性自由能关系的取代基参数，限定在明确分子骨架内，构建了可解释的机器学习模型。在低数据条件下，模型预测均方绝对误差小于6 kJ/mol，性能超越传统黑箱深度学习模型。模型的可解释性分析揭示了路易斯酸性的起源，并识别出通过取代基种类与位置调控酸性的可行策略。该工作连接机器学习与化学家思维，展示可解释模型如何启发分子设计并深化对化学反应性的理解。
原文摘要 · Abstract (English)
The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules with targeted properties. However, the focus has often been on creating highly efficient predictive models, sometimes at the expense of interpretability. In this study, we leverage explainable AI techniques to explore the rational design of boron-based Lewis acids, which play a pivotal role in organic reactions due to their electron-ccepting properties. Using Fluoride Ion Affinity as a proxy for Lewis acidity, we developed interpretable ML models based on chemically meaningful descriptors, including ab initio computed features and substituent-based parameters derived from the Hammett linear free-energy relationship. By constraining the chemical space to well-defined molecular scaffolds, we achieved highly accurate predictions (mean absolute error < 6 kJ/mol), surpassing conventional black-box deep learning models in low-data regimes. Interpretability analyses of the models shed light on the origin of Lewis acidity in these compounds and identified actionable levers to modulate it through the nature and positioning of substituents on the molecular scaffold. This work bridges ML and chemist's way of thinking, demonstrating how explainable models can inspire molecular design and enhance scientific understanding of chemical reactivity.
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