ABSTRACT Research in bioactive glasses (BGs) has traditionally been performed through trial-and-error experimentation. However, several modelling techniques will accelerate the discovery of new BGs as part of the ongoing endeavour to ‘decode the glass genome.’ Here, we critically review recent publications applying molecular dynamics simulations, machine learning approaches, and other modelling techniques for understanding BGs. We argue that modelling should be utilised more frequently in the design of BGs to achieve properties such as high bioactivity, high fracture strength and toughness, low density, and controlled morphology. Another challenge is modelling the biological response to biomaterials, such as their ability to foster protein adsorption, cell adhesion, cell proliferation, osteogenesis, angiogenesis, and bactericidal effects. The development of databases integrated with robust computational tools will be indispensable to these efforts. Future challenges are thus envisaged in which the compositional design, synthesis, characterisation, and application of BGs can be greatly accelerated by computational modelling.

中文翻译：

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生物活性玻璃的模型驱动设计：从分子动力学到机器学习

摘要 生物活性玻璃 (BG) 的研究传统上是通过反复试验进行的。然而，作为“解码玻璃基因组”的持续努力的一部分，几种建模技术将加速新BG的发现。在这里，我们批判性地回顾了最近发表的应用分子动力学模拟、机器学习方法和其他建模技术来理解 BG 的出版物。我们认为，在 BG 的设计中应该更频繁地使用建模，以实现诸如高生物活性、高断裂强度和韧性、低密度和受控形态等特性。另一个挑战是模拟对生物材料的生物反应，例如它们促进蛋白质吸附、细胞粘附、细胞增殖、成骨、血管生成和杀菌作用的能力。与强大的计算工具集成的数据库的开发对于这些努力是必不可少的。因此可以预见未来的挑战，其中 BG 的组成设计、合成、表征和应用可以通过计算建模大大加速。