자연어 처리를 이용한 배터리 소재 물성값 추출 자동화

Abstract

Batteries play an essential role in various fields of modern society. The duration of device usage and the speed of battery charging depend on the battery's capacity and performance. Particularly, the anode material, a key component in storing lithium ions, critically affects the charging speed and lifespan of the battery. Consequently, battery-related research is focused on developing new anode materials that offer higher capacities and improved performance. However, experimenting with different properties of batteries for the development of anode materials can be costly and pose safety risks. Additionally, the lack of systematic organization of numerous research findings often leads to redundant experiments. While referring to academic papers to obtain necessary property values is a solution, manual search and analysis by individuals have limitations and risk missing crucial information. To address these issues, this study has developed an automated model using Natural Language Processing (NLP) technology. It automatically extracts and organizes the names of materials and property values from a vast number of research papers on battery anode materials. This allows researchers to quickly identify the core topics of papers, clearly understand the content, track research trends, and swiftly grasp key keywords.