Tuning Network Connectivity of Silicate and Sodium Borosilicate Glasses by TiO2 for Enhanced Chemical Durability: Molecular Dynamics Simulation Investigations

Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning

Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relationship of glasses due to their enormous compositions and chemical space. Here, we address this problem and develop a metamodel of the composition-atomistic structure relationship of a class of glassy materials via a machine learning (ML) approach. Within this ML framework, an unsupervised deep learning technique, viz., a convolutional neural network (CNN) autoencoder, and a regression algorithm, viz. random forest (RF), are integrated into a fully automated pipeline to predict the spatial distribution of atoms in a glass. The RF regression model predicts the pair correlation function of a glass in a latent space. Subsequently, the decoder of the CNN converts the latent space representation to the actual pair correlation function of the given glass. The atomistic structures of silicate (SiO2) and sodium borosilicate (NBS) based glasses with varying compositions and dopants are collected from molecular dynamics (MD) simulations to establish and validate this ML pipeline. The model is found to predict the atom pair correlation functions for many unknown glasses very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of the composition-atomistic structure relationship of glasses and other materials.

Regulated contribution of local and systemic immunity to new bone regeneration by modulating B/Sr concentration of bioactive borosilicate glass

The local immune response induced by bioactive borosilicate glass (BG) plays a vital role in bone regeneration, but its effect in the systemic immune response of distal tissues, such as spleen, remains unknown. In this study, the network structures and the relative theoretical structural descriptors (F_net) of the novel BG composition containing boron (B) and strontium (Sr) were calculated and stimulated by molecular dynamics (MD) simulation, and the linear relationships of F_net and B and Sr releasing rate in pure water and simulate body fluid were built. Next, the synergistic effects of the released B and Sr on promoting osteogenic differentiation, angiogenesis, and macrophage polarization were analyzed in vitro and convinced in rats skull models in vivo. Results show that the optimal synergistic effects of B and Sr both in vitro and in vivo released from 1393B2Sr8 BG increased vessel regeneration, modulated M2 macrophages polarization and promoted new-bone formation. Interestingly, the 1393B2Sr8 BG was found to mobilize monocytes from the spleen to the defects and subsequently modulate them into M2 macrophages. Then, these modulated cells cycled from the bone defects back to the spleen. To analyze the necessity of spleen-derived immune cells in bone regeneration, two contrasting rat models (with/without spleen) of skull defects were furtherly established. As results, rats without spleen had fewer M2 macrophages surrounding skull defects and the bone tissues recovered more slowly, indicating the beneficial effects on bone regeneration of circulating monocytes and polarized macrophages provided by spleen. The present study provides a new approach and strategy in optimizing complex composition of novel BG and sheds light on the importance of spleen through modulating systemic immune response to contribute to local bone regeneration.