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Vol. 17 (2025)

An Efficient Deep Learning Framework for Revealing the Evolution of Characterization Methods in Nanoscience

https://doi.org/10.1007/s40820-025-01807-z
Hui‑Cong Duan
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Long‑Xing Lin
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Ji‑Chun Wang
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Tong‑Ruo Diao
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Sheng‑Jie Qiu
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Bi‑Jun Geng
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Jia Shi
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Shu Hu
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China
Yang Yang
Institute of Artificial Intelligence, Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, People’s Republic of China

Corresponding Author: Yang Yang

yangyang@xmu.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 295

Abstract

Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data. To date, most text mining methods are restricted to specific literature information, resulting in incomplete knowledge graphs. Here, we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science. Leveraging this method, we construct a knowledge graph in the field of Raman spectroscopy. The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model. Our method improves the topic coherence with a minimum growth rate of 100% compared to the traditional text mining method. It outperforms the traditional text mining method on the diversity, and its growth rate ranges from 0 to 126%. The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry. It is versatile in revealing the distribution of topics, establishing the similarity and inheritance relationships, and identifying the important moments in the history of Raman spectroscopy. Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.


Highlights:
1 A framework combining the citation analysis with topic modeling is designed to construct the knowledge graph of a research field.
2 An extensible tokenizer is designed to improve the universality of the framework, and the performance of topic recognition is superior to that of the traditional method.
3 The detailed evolutionary paths of Raman spectroscopy technology are demonstrated, and the significant publications in the Raman spectroscopy are identified.

Keywords

Nanostructure Deep learning Data-driven Raman Nanoscience
Duan, Hui‑Cong, Long‑Xing Lin, Ji‑Chun Wang, Tong‑Ruo Diao, Sheng‑Jie Qiu, Bi‑Jun Geng, Jia Shi, Shu Hu, and Yang Yang. 2025. “An Efficient Deep Learning Framework for Revealing the Evolution of Characterization Methods in Nanoscience”. Nano-Micro Letters 17 (June):295. https://doi.org/10.1007/s40820-025-01807-z.
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