Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications

Abstract

Machine learning (ML), as an advanced data analysis tool, simulates the learning process of the human brain, enabling the extraction of features, discovery of patterns, and making accurate predictions or decisions from complex data. In the field of nanomaterial design, the application of ML technology not only accelerates the discovery and performance optimization of nanomaterials but also promotes the innovation of materials science research methods. Bibliometrics, as a research method based on quantitative analysis, provides us with a macro perspective to observe and understand the application of ML technology in nanomaterial design by statistically analyzing various indicators in the scientific literature. This paper quantitatively analyzes the literature related to ML-driven nanomaterial design from seven dimensions, revealing the importance and necessity of ML technology in nanomaterial design. It also systematically analyzes the diversified applications of the combination of ML technology and nanomaterial technology with the design of suitable ML algorithms being key to enhancing the performance of nanomaterials. In addition, this paper discusses current challenges and future development directions, including data quality and data set construction, algorithm innovation and optimization, and the deepening of interdisciplinary cooperation. This review not only provides researchers with a macro perspective to observe the current state and development trends of the field but also provides ideas and suggestions for future research. This is of significant importance and value for promoting scientific progress in the field of nanomaterial design, fostering the in-depth development of interdisciplinary research, and accelerating the innovative application of material technologies.