Deep Learning for Medical Materials: Review and Perspective

Abstract

Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists.

Thermomechanical properties of silica–epoxy nanocomposite modified by hyperbranched polyester: A molecular dynamics simulation

“…However, traditional experimental methods lack the means to characterize the relationship and mechanism. 27 In recent years, computer simulations have been widely used for the analysis of materials, including machine learning [28][29][30][31][32][33][34][35] and molecular dynamics (MD) simulations. 36,37 Molecular dynamics simulation can build materials at an atomic scale, which can greatly reduce the experimental cost and development cycle.…”