The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R² of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.

中文翻译：

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过渡态理论启发的神经网络估计深共晶溶剂的粘度

缺乏准确的方法来预测溶剂材料的粘度，尤其是那些具有复杂相互作用的材料，仍然没有得到解决。深共熔溶剂（DESs）是一类新兴的绿色溶剂，其粘度数据严重缺乏，导致其应用仍停留在随机试错阶段，难以实现工业化规模化应用. 在这项工作中，我们展示了基于过渡态理论启发的神经网络 (TSTiNet) 对 DES 粘度的成功预测。TSTiNet 采用多层感知器 (MLP) 进行过渡态理论启发方程 (TSTiEq) 参数计算和验证，使用迄今为止最全面的 DESs 粘度数据集。对于 TSTiEq 的能量参数，R ²为 0.9805)。与传统的机器学习方法相比，TSTiNet具有更好的泛化能力，并且在热力学公式约束下显着降低了预测的最大相对偏差。它只需要 DESs 的结构信息，是可用于 DESs 粘度预测的最准确和最可靠的模型。