Efforts to reduce the carbon footprint associated with cement and concrete production have resulted in a number of promising lower-emission alternatives. Still, research has emphasized a small subset of potentially useful precursor materials. With the goal of expanding the precursor pool, this work presents results of parallel literature mining and rate modeling activities. As a result of literature mining, materials with appropriate SiO2, Al2O3, and CaO concentrations were assembled into a comprehensive, representative ternary diagram. 23 000+ materials were extracted from 7000 journal articles, and 7500 materials from 6000 articles with 80 ≤ SiO2 + Al2O3 + CaO ≤105 wt% automatically classified. Both supervised and semi-supervised models were used for dissolution rate prediction of glassy materials with all models pulling from a single data set (n = 802 reported dissolution rates from 105 different glasses). Supervised modeling utilized linear and decision tree regressions to determine features most predictive of dissolution rate, resulting in log-linear relationships between rate and pH, inverse temperature (1/K), and non-bridging oxygen per tetrahedron (NBO/T). Semi-supervised modeling was observed to be more robust to broader feature inclusion, providing similar predictive ability with a relatively larger set of descriptive features. Most importantly, results indicated that models trained on data from disparate scientific communities were adequately predictive (RMSE ≈ 1), particularly under pH ≥7 conditions relevant to the cement and alkali activation communities.