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

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

Research output: Contribution to journalArticle

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)
JournalInternational Materials Reviews
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Bioactive glass
Learning systems
Molecular dynamics
Fracture toughness
Cell adhesion
Cell proliferation
Biocompatible Materials
Bioactivity
Biomaterials
Genes
Proteins
Adsorption
Glass
Computer simulation

All Science Journal Classification (ASJC) codes

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

Cite this

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Model-driven design of bioactive glasses : from molecular dynamics through machine learning. / Montazerian, Maziar; Zanotto, Edgar D.; Mauro, John C.

In: International Materials Reviews, 01.01.2019.

Research output: Contribution to journalArticle

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