# Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models
## 6 Conclusions  
In this study, the DF and analytical models were demonstrated to predict the dissolution rate of C\({}_{3}\)S. The DF model was used to predict the dissolution rate of C\({}_{3}\)S in relation to temperature, ion concentration in solvent, and pH, which can be directly obtained from experimental measurements. To the best of the authors' knowledge, this is the first study to employ ML to predict the dissolution rate of C\({}_{3}\)S when it is undersaturated with respect to a wide range of solvents. Another novel point of this study is the leveraging of the DF model for evaluating the influence of input variables and using such knowledge to develop an analytical model. The database was collected from two distinct experimental setups: _reactor connected to ICP spectrometer_ and _flow chamber with VSI_. The DF model was rigorously trained by 75% of the parent database that consisted of 292 data records. Subsequently, the model was tested against the remaining 25% of the data records to evaluate prediction performance. The results demonstrated that the DF model was able to yield reliable predictions, with an \(R^{2}\) value of approximately 0.97, of C\({}_{3}\)S dissolution rate in the undersaturated solution. The DF model allows researchers to acquire the dissolution rate of C\({}_{3}\)S by simply knowing the ion concentration and temperature of solvents without the cumbersome dissolution experiments. The DF model was also employed to evaluate the influence of input variables on the dissolution rate of C\({}_{3}\)S. It was found that the pH value of solvents and the concentration of Ca\({}^{2+}\) exerted significant influences on the dissolution process, while the concentration of silicate ions had little influence. The analytical model (only using data from the _reactor connected to ICP spectrometer_ method) was classified into two scenarios: _generic_ solvent and _alkaline_ solvent. The coefficients of the _generic_ solvent and _alkaline_ solvent scenarios were optimized by 92 data records and 75 data records, respectively. The physiochemical properties--which were used as inputs for both scenarios--comprised SSA of C\({}_{3}\)S, temperature, ion activity of Ca\({}^{2+}\), OH\({}^{-}\), and H\({}^{+}\), ionic strength of solvent, and degree of undersaturation. The results showed that the analytical model was able to produce reliable predictions of _generic_ solvent with R \(\approx\) 0.83 and _alkaline_ solvent with R \(\approx\) 0.96 when all coefficients were rigorously optimized. Unlike ML, the analytical model can quantitively interpret aqueous chemistry-dissolution correlations. Overall, the DF model is an apposite platform that can be used in the future to study the dissolution kinetics of cementitious materials. A large volume and diverse database can further enhance prediction accuracy. By incorporating a wide range of data, the model can better capture the complex dissolution behavior of cementitious materials. This can improve the reliability of the model's predictions, allowing it to be used more effectively in the design of cementitious materials. Overall, the DF model has the potential to be a valuable tool for studying the dissolution kinetics of cementitious materials. Conceptualization, development, training, and validation of machine learning and analytical models, and preparation of original manuscript, T.H. ; development of analytical model and preparation of original manuscript, S.A.P. ; manuscript review and editing, A.R. ; supervision, manuscript review and editing; and funding acquisition, J.H. ; supervision, manuscript review and editing, and funding acquisition, G.S. ; conceptualization, manuscript review and editing, and funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.