Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.

溶剂效应影响化学过程的所有阶段，调节中间体和过渡态的稳定性，以及改变反应速率和产物比例。然而，准确地模拟这些影响仍然具有挑战性。在这里，我们提出了一种生成反应性机器学习势来模拟溶液中化学过程的通用策略。我们的方法将主动学习与基于描述符的选择器和自动化相结合，从而能够构建跨越相关化学和构象空间的数据高效训练集。我们应用这种策略来研究水和甲醇中的狄尔斯-阿尔德反应。生成的机器学习势使我们能够获得与实验数据一致的反应速率，并分析这些溶剂对反应机理的影响。我们的策略为溶液中化学反应的常规建模提供了一种有效的方法，为有效研究复杂化学过程开辟了途径。