where A, B, S, δ , and δ 2 H are the Abraham's overall hydrogenbond acidity, Abraham's overall hydrogen-bond basicity, dipolarity/polarisability, and Hildebrand solubility parameter, respectively, of the solvent, and c 0 , c A , c B , c S , c δ , and c H are the coefficients that need to be estimated via multiple linear regression (MLR). Estimating the parameters in the MLR model with high accuracy can be challenging because only a small number of experiments can often be conducted, restricting its predictive capacity. To address this challenge, DoE-QM-CAMD first selects an information-rich set of (computer) experiments using the Doptimality criterion. These initial experiments, which are observed to cover a wider range of solvent properties, are then used to train an initial MLR model. Subsequently, to refine the MLR model and enhance its predictability around the optimal solvent region, iterative optimization is undertaken to identify the best solvent (i.e., the one that gives the highest reaction rate) based on the current MLR model. Should a new solvent be identified, the MLR model undergoes re-fitting with the updated experimental set that consists of the newly identified optimal solvent and the solvents in the original set. The iterative process terminates when the best solvent, determined by optimizing the MLR model, has been sampled previously. Upon convergence of the MLR model, the top 10 solvents are determined by re-initializing and optimizing the problem, wherein integer cuts are added to exclude pre-viously identified solutions. We note that the active-learning-like iterative process of DoE-QM-CAMD after the initial experiments relies solely on the newly fitted MLR model with limited capabilities for exploration. Also, as a grey box algorithm, it can propagate the biases.