Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.

调节Mg降解行为的有机小分子构成良性且有用的材料，可以改变轻金属材料用于特定应用的使用环境。可以通过基于机器学习的定量结构-性质关系模型来探索潜在有效化合物的广阔化学空间，从而加速发现有效的溶出调节剂。在这里，我们演示了如何通过结构相似性和示意图绘制无监督的大量潜在Mg溶出度调节剂簇，可以使用核岭回归模型预测其实验性能。我们将这种方法的预测准确性与先前的人工神经网络研究进行了比较。通过对10种未经测试的化合物的溶出调节性能进行盲目预测，我们确认了我们数据驱动模型的稳健性。最后，提出了一种工作流程，该工作流程有助于从商业数据库中自动发现具有所需溶解调节特性的化学药品。随后，我们通过对五种化学品的盲目验证来证明这一概念。