Magnesium oxysulfate (MOS) cement is a typical eco-friendly cementitious material, which presents excellent performances. In this work, a novel multiscale modeling strategy is proposed to simulate the hydration and pore structure of MOS cement system. This work collected and evaluated the Gibbs free energy of formation for main hydrates and equilibrium constant of main reactions in MOS cement system based on a first principle calculation using Material Studio. Followingly, the equilibrium phase compositions of MOS cement system were simulated through PHREEQC to investigate the molar ratio dependence of equilibrium phase compositions. Results showed that large M (MgO/MgSO4) was beneficial for the formation of 5Mg(OH)2·MgSO4·7H2O (Phase 517) and large H (H2O/MgSO4) tended to decompose MOS cement paste and cause leaching. The microstructure-based method visualized the hydration status of MOS cement systems at initial and ultimate stages via MATLAB and the results showed that large M was significant to reduce porosity, and similar results for the case of small H. Fractal analysis confirms that fractal dimension of pore structure (Df) was significantly decreased after the hydration of MOS and was positively correlated to the porosity of the paste. In addition, it can be referred that large M and small H were beneficial for modifying the microstructure of MOS paste by decreasing the value of Df.

Magnesium phosphate cement (MPC) paste is hardened by the acid–base reaction between magnesium oxide and phosphate. This work collects and evaluates the thermodynamic data at 25 ℃ and a pressure of 0.1 MPa and establishes the hydration reaction model of MPC pastes. The influence of the magnesium–phosphorus molar (M/P) ratio and water-to-binder (W/B) ratio on the hydration product is explored by the thermodynamic simulation. Following this, the initial and ultimate states of the hydration state of MPC pastes are visualized, and the porosity of different pastes as well as fractal analysis are presented. The result shows that a small M/P ratio is beneficial for the formation of main hydration products. The boric acid acts as a retarder, has a significant effect on lowering the pH of the paste, and slows down the formation of hydration products. After the porosity comparison, it can be concluded that the decreasing of M/P and W/B ratios helps reduce porosity. In addition, the fractal dimension Df of MPC pastes is positively proportional to the porosity, and small M/P ratios as well as small W/B ratios are beneficial for reducing the Df of MKPC pastes.

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In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Highlights We present an up to date review of the application of artificial intelligence in materials modeling and design.  We comprehensively discuss past and recent applications in modeling and design of polymers, metals, ceramics and other materials. We identify current research focal points, challenges, and opportunities for the application of artificial intelligence in materials modeling and design.

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