We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol−1 (acceptors), and 2.3 kJ mol−1 (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.

中文翻译：

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使用第一原理相互作用自由能生成的大量且多样化的训练数据来建立氢键供体和受体强度的机器学习模型

我们提出了氢键受体 (HBA) 和氢键供体 (HBD) 强度的机器学习 (ML) 模型。溶液中与参比分子 4-氟苯酚和丙酮形成 1:1 氢键复合物的量子化学 (QC) 自由能作为我们的目标值。我们的受体和供体数据库是有记录以来最大的，分别有 4426 个和 1036 个数据点。在扫描径向原子描述符和 ML 方法后，我们最终训练的 HBA 和 HBD ML 模型在实验测试集上分别实现了 3.8 kJ mol−1（受体）和 2.3 kJ mol−1（供体）的 RMSE。该性能与之前在实验氢键自由能上训练的模型相当，表明分子QC数据可以替代实验。其潜在影响可能会导致湿实验室化学完全取代通过 QC 测定 HBA/HBD 强度。作为我们的 ML 模型的可能化学应用，我们在两个关于分子内氢键趋势的案例研究中强调了我们预测的 HBA 和 HBD 强度作为可能的描述符。