Machine Learning as a Tool to Design Glasses with Controlled Dissolution for Healthcare Applications
Abstract
The advancement of glass science has played a pivotal role in enhancing the quality and length of human life. However, with an ever-increasing demand for glasses in a variety of healthcare applications -- especially with controlled degradation rates -- it is becoming difficult to design new glass compositions using conventional approaches. For example, it is difficult, if not impossible, to design new gene-activation bioactive glasses, with controlled release of functional ions tailored for specific patient states, using trial-and-error based approaches. Notwithstanding, it is possible to design new glasses with controlled release of functional ions by using artificial intelligence-based methods, for example, supervised machine learning (ML). In this paper, we present an ensemble ML model for reliable prediction of time- and composition-dependent dissolution behavior of a wide variety of oxide glasses relevant for various biomedical applications. A comprehensive database, comprising of over 1300 data-records consolidated from original glass dissolution experiments, has been used for training and subsequent testing of prediction performance of the ML model. Results demonstrate that the ensemble ML model can predict chemical degradation behavior of glasses in aqueous solutions over a wide range of pH relevant for their usage in a human body where the environment can be highly acidic (for example, pH = 3), for example, due to secretion of citric acid by osteoclasts, or highly alkaline (pH ≈ 10) due to the release of alkali cations from bioactive glasses. Outcomes of this study can be leveraged to design glasses with controlled dissolution behavior in various biological environments. Statement of Significance: In this paper, we present an ensemble machine learning (ML) model for prediction of dissolution behavior of a wide variety of oxide glasses relevant for various biomedical applications. The results demonstrate that the ML model can predict the chemical degradation behavior of glasses in aqueous solutions over a wide range of pH relevant for their usage in a human body where the environment can be highly acidic (for example, pH = 3), for example, due to secretion of citric acid by osteoclasts, or highly alkaline (pH ≈ 10) due to the release of alkali cations from bioactive glasses. Outcomes of this study can be leveraged to design new biomedical glasses with controlled (desired) dissolution behavior in various biological environments.
Recommended Citation
T. Han et al., "Machine Learning as a Tool to Design Glasses with Controlled Dissolution for Healthcare Applications," Acta Biomaterialia, vol. 107, pp. 286 - 298, Acta Materialia Inc, Apr 2020.
The definitive version is available at https://doi.org/10.1016/j.actbio.2020.02.037
Department(s)
Materials Science and Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Additive Regression; Biomedical; Ensemble Machine Learning; Glass Dissolution; Random Forest
International Standard Serial Number (ISSN)
1742-7061; 1878-7568
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Acta Materialia Inc, All rights reserved.
Publication Date
01 Apr 2020
PubMed ID
32114183
Comments
Computational tasks were conducted in the Materials Research Center (MRC) of Missouri S&T. This material is based upon the work supported by the National Science Foundation under Grant No. DMR: 1507131, CMMI: 1661609, and CMMI: 1932690, and the Leonard Wood Institute (LWI).