Aspects of Admixture Research: On the Use of Machine Learning in Superplasticizer Chemistry

The use of superplasticizers in concrete, especially polycarboxylate ethers (PCE), has delivered the ability to easily achieve low water to cement ratios and thereby either higher strength or lower cement contents. In the last years, significant progress has been made with regard to understanding the structure‐activity relationship of the interaction of PCE and cement. For example, scaling laws have been derived for the size of adsorbed PCE, the magnitude of the steric interaction force, for the retardation of cement hydration by PCEs and more recently for competitive adsorption. While this is extremely useful, the picture is not fully complete yet.

In this contribution, we wish to highlight some recent work in the field of data analysis of PCE. Inspired by a very early machine‐learning study of concrete formulations, we extracted structural PCE data together with rheology data from the literature. We compare PCE performance across studies and attempt to uncover underlying structure‐activity‐relationships (using machine learning models). It turns out that the data set quality and quantity is not yet sufficient to establish reliable models.