Dynamics of Confined Water and its Interplay with Alkali Cations in Sodium Aluminosilicate Hydrate Gel: Insights from Reactive Force Field Molecular Dynamics

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibit a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5-20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers

Prediction of Concrete Strengths Enabled by Missing Data Imputation and Interpretable Machine Learning

Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development of robust ML-based predictive models challenging. Besides, as the degree of complexity in these ML models increases, the interpretation of the results becomes challenging. These interpretations of results are critical towards the development of efficient materials design strategies for enhanced materials performance. To address these challenges, this paper implements different data imputation approaches for enhanced dataset completeness. The imputed dataset is leveraged to predict the compressive and tensile strength of concrete using various hyperparameter-optimized ML approaches. Among all the approaches, Extreme Gradient Boosted Decision Trees (XGBoost) showed the highest prediction efficacy when the dataset is imputed using k-nearest neighbors (kNN) with a 10-neighbor configuration. To interpret the predicted results, SHapley Additive exPlanations (SHAP) is employed. Overall, by implementing efficient combinations of data imputation approach, machine learning, and data interpretation, this paper develops an efficient approach to evaluate the composition-strength relationship in concrete. This work, in turn, can be used as a starting point toward the design and development of various performance-enhanced and sustainable concretes

Fracture Toughness of Fly Ash-Based Geopolymer Gels: Evaluations Using Nanoindentation Experiment and Molecular Dynamics Simulation

This paper presents the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash. While the fracture toughness of N-A-S-H is obtained experimentally from nanoindentation experiment implementing the principle of conservation of energy, the numerical investigation is performed via reactive force field molecular dynamics. A statistically significant number of indentations are performed on geopolymer paste yielding frequency distribution of Young’s modulus. Four distinct peaks are observed in the frequency distribution plot from which the peak corresponding to N-A-S-H was separated using statistical deconvolution technique. The young’s modulus of N-A-S-H, thus obtained from statistical deconvolution shows excellent match with the values reported in the literature, thus confirming successful identification of indentations corresponding to N-A-S-H. From the load-penetration depth responses of N-A-S-H, fracture toughness was obtained following the principle of conservation of energy. The experimental fracture toughness shows good correlation with the simulated fracture toughness of N-A-S-H, obtained from reactive force field molecular dynamics. The fracture toughness of N-A-S-H presented in this paper paves the way for multiscale simulation-based design of tougher geopolymer binders

Realistic atomic structure of fly ash-based geopolymer gels: Insights from molecular dynamics simulations

Geopolymers, synthesized through alkaline activation of aluminosilicates, have emerged as a sustainable alternative for traditional ordinary Portland cement. In spite of the satisfactory mechanical performance and sustainability-related benefits, the large scale acceptance of geopolymers in the construction industry is still limited due to poor understanding of the composition-property relationships. Molecular simulation is a powerful tool to develop such relationships, provided that the adopted molecular structure represents the experimental data effectively. Toward this end, this paper presents a new molecular structure of sodium aluminosilicate hydrate geopolymer gels, inspired from the traditional calcium silicate hydrates gel. In contrast to the existing model—where water is uniformly distributed in the structure—we present a layered-but-disordered structure. This new structure incorporates water in the interlayer space of the aluminosilicate network. The structural features of the new proposed molecular structure are evaluated in terms of both short- and medium-range order features such as pair distribution functions, bond angle distributions, and structure factor. The structural features of the newly proposed molecular structure with interlayer water show better correlation with the experimental observations as compared to the existing traditional structure signifying an increased plausibility of the proposed structure. The proposed structure can be adopted as a starting point toward the realistic multiscale simulation-based design and development of geopolymers

Dynamics of confined water and its interplay with alkali cations in sodium aluminosilicate hydrate gel: insights from reactive force field molecular dynamics

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibits a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5–20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers

Fracture Toughness of Fly Ash-Based Geopolymer Gels: Evaluations Using Nanoindentation Experiment and Molecular Dynamics Simulation

This paper presents the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash. While the fracture toughness of N-A-S-H is obtained experimentally from nanoindentation experiment implementing the principle of conservation of energy, the numerical investigation is performed via reactive force field molecular dynamics. A statistically significant number of indentations are performed on geopolymer paste yielding frequency distribution of Young’s modulus. Four distinct peaks are observed in the frequency distribution plot from which the peak corresponding to N-A-S-H was separated using statistical deconvolution technique. The young’s modulus of N-A-S-H, thus obtained from statistical deconvolution shows excellent match with the values reported in the literature, thus confirming successful identification of indentations corresponding to N-A-S-H. From the load-penetration depth responses of N-A-S-H, fracture toughness was obtained following the principle of conservation of energy. The experimental fracture toughness shows good correlation with the simulated fracture toughness of N-A-S-H, obtained from reactive force field molecular dynamics. The fracture toughness of N-A-S-H presented in this paper paves the way for multiscale simulation-based design of tougher geopolymer binders

Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium–silicate–hydrate (C–S–H) gel—the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C–S–H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C–S–H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C–S–H nanostructures to design efficient cementitious binders with targeted properties

Fracture toughness of sodium aluminosilicate hydrate (NASH) gels: Insights from molecular dynamics simulations

This paper evaluates the fracture toughness of sodium aluminosilicate hydrate (N-A-S-H) gel formed through alkaline activation of fly ash via molecular dynamics (MD) simulations. The short- and medium-range order of the constructed N-A-S-H structures shows good correlation with the experimental observations, signifying the viability of the N-A-S-H structures. The simulated fracture toughness values of N-A-S-H (0.4–0.45 MPa m0.5) appear to be of the same order as the available experimental values for fly ash-based geopolymer mortars and concretes. These results suggest the efficacy of the MD simulation toward obtaining a realistic fracture toughness of N-A-S-H, which is otherwise very challenging to obtain experimentally, and no direct experimental fracture toughness values are yet available. To further assess the fracture behavior of N-A-S-H, the number of chemical bonds formed/broken during elongation and their relative sensitivity to crack growth are evaluated. Overall, the fracture toughness of N-A-S-H presented in this paper paves the way for a multiscale simulation-based design of tougher geopolymers

Elucidating the Costitutive Relationship of Calcium-Silicate-Hydrate Gel Using High Throughput Reactive Molecular Simulations and Machine Learning

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium–silicate–hydrate (C–S–H) gel—the primary binding phase in concrete formed via the hydration of ordinary Portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C–S–H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C–S–H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C–S–H nanostructures to design efficient cementitious binders with targeted properties

Elucidating the auxetic behavior of cementitious cellular composites using finite element analysis and interpretable machine learning

With the advent of 3D printing, auxetic cellular cementitious composites (ACCCs) have recently garnered significant attention owing to their unique mechanical performance. To enable seamless performance prediction of the ACCCs, interpretable machine learning (ML)-based approaches can provide efficient means. However, the prediction of Poisson’s ratio using such ML approaches requires large and consistent datasets which is not readily available for ACCCs. To address this challenge, this paper synergistically integrates a finite element analysis (FEA)-based framework with ML to predict the Poisson’s ratios. In particular, the FEA-based approach is used to generate a dataset containing 850 combinations of different mesoscale architectural void features. The dataset is leveraged to develop an ML-based prediction tool using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. To shed light on the relative influence of the design parameters on the auxetic behavior of the ACCCs, Shapley additive explanations (SHAP) is employed, which establishes the volume fraction of voids as the most influential parameter in inducing auxetic behavior. Overall, this paper develops an efficient approach to evaluate geometry-dependent auxetic behaviors for cementitious materials which can be used as a starting point toward the design and development of auxetic behavior in cementitious composites