Neural Networks in Materials Science.

TLDR

Materials science faces complex problems that are not yet amenable to scientific treatment, and engineering must achieve cost‑effective solutions, but small models are insufficient; neural‑network analysis offers a regression/classification approach that may help resolve these difficulties. The paper aims to show that neural‑network analysis can help resolve difficult materials‑science problems by providing regression or classification models toward long‑term solutions. The authors introduce neural networks and review their applications in materials science.

Abstract

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach objectives in a cost and time-effective way. Any model which deals with only a small part of the required technology is therefore unlikely to be treated with respect. Neural network analysis is a form of regression or classification modelling which can help resolve these difficulties whilst striving for longer term solutions. This paper begins with an introduction to neural networks and contains a review of some applications of the technique in the context of materials science.