Model-driven design of bioactive glasses: from molecular dynamics through machine learning

Maziar Montazerian, Edgar D. Zanotto, John C. Mauro

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)297-321
Number of pages25
JournalInternational Materials Reviews
Volume65
Issue number5
DOIs
StatePublished - Jul 3 2020

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

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