인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The overwhelming volume of unstructured scientific literature presents a fundamental bottleneck to materials discovery, where critical data on synthesis and properties remain locked in text. Here, a closed-loop framework that integrates automated knowledge extraction with interpretable machine learning and targeted experimental validation is presented. This approach is centered on a novel data extraction pipeline, which combines a prompt-engineered large language model with a model ensemble strategy, systematically optimized to interpret complex materials science narratives. When deployed to construct a database for defect-engineered carbon nitride photocatalysts, the system achieved 90% accuracy and recall for key parameters. Analysis of the high-fidelity dataset enabled reliable machine learning models to identify specific surface area (170 m<sup>2</sup> g<sup>-1</sup>) and bandgap (≈2.31 eV) as dominant performance parameters. Crucially, SHapley Additive exPlanations analysis elucidated a non-monotonic relationship for bandgap, identifying an optimal range of 2.2-2.4 eV and quantifying the fundamental trade-off between light absorption and charge recombination. These data-driven insights guided the synthesis of representative materials, with experimental hydrogen evolution rates deviating by less than 5% from predictions. This work establishes a scalable and transferable paradigm, transforming fragmented literature into actionable intelligence and offering a powerful strategy for accelerating the development of functional materials.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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