Spiekermann Kevin A, Dong Xiaorui, Menon Angiras, Green William H, Pfeifle Mark, Sandfort Frederik, Welz Oliver, Bergeler Maike
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
BASF Digital Solutions GmbH, Ludwigshafen am Rhein 67061, Germany.
J Phys Chem A. 2024 Oct 3;128(39):8384-8403. doi: 10.1021/acs.jpca.4c04121. Epub 2024 Sep 19.
Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.
反应势垒和溶剂效应的定量估计对于建立动力学机制和预测反应结果至关重要。在此，我们创建了一个包含5600个独特基元自由基反应的新数据集，这些反应是使用M06 - 2X/def2 - QZVP//B3LYP - D3(BJ)/def2 - TZVP理论水平计算得到的。使用TPSS/def2 - TZVP对每个物种进行构象搜索。利用COSMO - RS考虑溶剂化效应，获得这些自由基反应在40种常见溶剂中的活化吉布斯自由能和反应吉布斯自由能。这些平衡反应涉及H、C、N、O和S元素，包含多达19个重原子，并具有原子映射的SMILES。所有过渡态均通过内禀反应坐标计算进行验证。接下来，我们训练一个深度图网络，仅使用反应物和产物的原子映射SMILES以及溶剂的SMILES，直接估计气相和溶液相中活化吉布斯自由能和反应吉布斯自由能。这种简单的输入表示避免了推理过程中对反应物、过渡态和产物结构进行计算成本高昂的优化，使我们的模型非常适合高通量预测化学，并能快速为（逆）合成规划工具提供信息。为了正确衡量模型性能，我们报告了内插和外推数据分割的结果，并与几个基线模型进行了比较。在训练和测试过程中，通过纳入每个反应的反向方向和具有不同共振结构的变体来扩充数据集。数据扩充后，我们有大约200万个条目来训练模型，该模型在溶液中活化吉布斯自由能的测试集平均绝对误差为1.16 kcal/mol。我们预计该模型将加速高通量筛选的预测，以快速识别溶液中的相关反应，并且我们的数据集将作为未来研究的基准。
