The Dawn of Metamaterial Engineering Predicted via Hyperdimensional Keyword Pool and Memory Learning

Abstract

AbstractMetamaterials research has been ongoing for more than 20 years and it has gained much public and scientific interest. There have been many experts forecasting on the road ahead for metamaterials, notably, in lieu of “knowledge tree” in 2010. Ten years on, it is proposed to re‐examine these claims by using automated computer tools, such as natural language processing (NLP), to extract research information for processing and analyzing from unstructured texts in publications. In this study, a fully auto‐generated database of 43 678 abstracts related to metamaterials published between 2000 and 2021 using Scopus Search API (Application Programming Interface) is built. Applying word embedding, each keyword is studied in a hyperdimensional vector space and clusters so that their relationships can be visualized for assessing the popularity and trends of research themes. A neural network model developed based on the encoder–decoder long short‐term memory (LSTM) architecture is finally trained to predict future directions and theme evolutions in the next four years for selected topics. This study not only provides vital information in terms of impact of metamaterials research but also lays down a solid foundation for the development of future metamaterial research roadmap in the form of Gartner hype cycle.