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

Abstract

Metamaterials 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.

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

“…State-of-the-art natural language processing (NLP) tools make the process of text mining from the literature achievable on a large scale, without the need for a coalition of human annotators, and such methods have recently been applied to the materials science literature. − ,− Most of the efforts in this subfield have been in the extraction of relevant material entities, such as chemical formulas, material properties, and processing conditions; meanwhile, efforts on large-scale text mining of synthesis pathways remain limited, largely since most studies in text mining synthesis tend to assume pure target formation, without the consideration of incomplete reactions or reactions that form persistent impurity phases. Exploratory synthesis, on the other hand, rarely yields phase-pure targets.…”