A machine learning approach to understanding and optimizing complex physical systems is presented in the context of polymeric dispersants.

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The massive scale of concrete construction constrains the raw materials’ feedstocks that can be considered – requiring both universal abundance but also economical and energy-efficient processing. While significant improvements– from more efficient cement and concrete production to increased service life – have been realized over the past decades through traditional research paradigms, non-incremental innovations are necessary now to meet increasingly urgent needs, at a time when innovations in materials create even greater complexity. Data science is revolutionizing the rate of discovery and accelerating the rate of innovation for material systems. This review addresses machine learning and other data analytical techniques which utilize various forms of variable representation for cementitious systems. These techniques include those guided by physicochemical and cheminformatics approaches to chemical admixture design, use of materials informatics to develop process-structure-property linkages for quantifying increased service life, and change-point detection for assessing pozzolanicity in candidate supplementary cementitious materials (SCMs). These latent variables, coupled with approaches to dimensionality reduction driven both algorithmically as well as through domain knowledge, provide robust feature representation for cement-based materials and allow for more accurate models and greater generalization capability, resulting in a powerful design tool for infrastructure materials.

Lignosulfonates are a leading water reducer for cement and concrete, but their mechanism of controlling aggregation is primarily electrostatic. In contrast, polycarboxylate ether superplasticisers function via steric interactions and impart high degrees of fluidity at low concentrations. In this study, it is demonstrated that grafting lignosulfonate with mono-functional, low molecular weight poly(ethylene oxide) significantly enhances performance in type I/II Portland cement pastes as well as in models of mortar. Polymer grafting did not affect the zeta potential but altered the adsorption profile onto Portland cement, with poly(ethylene oxide)-grafted lignosulfonate reaching saturation rather than forming a multi-layer structure as observed with lignosulfonates. The grafted lignosulfonates increased slump values by up to 30% compared with non-grafted lignosulfonate while allowing for significant reductions in water content. Poly(ethylene oxide)-grafted lignosulfonate represents a hybrid superplasticiser that offers the advantages of both lignosulfonates and polycarboxylate ethers.

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