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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Montazerian, Maziar [1] ; Zanotto, Edgar D. [1] ; Mauro, John C. [2]
Total Authors: 3
[1] Fed Univ Sao Carlos UFSCar, Dept Mat Engn DEMa, Ctr Res Technol & Educ Vitreous Mat CeRTEV, Sao Carlos, SP - Brazil
[2] Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 - USA
Total Affiliations: 2
Document type: Review article
Web of Science Citations: 4

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. (AU)

FAPESP's process: 15/13314-9 - Development and Characterization of Tough Bioactive Glass-ceramics
Grantee:Maziar Montazerian
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 13/07793-6 - CEPIV - Center for Teaching, Research and Innovation in Glass
Grantee:Edgar Dutra Zanotto
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC