\u3000A paper by researchers from SAKURA internet Research Center, the in-house research organization of SAKURA internet Inc. (hereinafter, “SAKURA internet,” Headquarters: Osaka City, Osaka; Founder, President & CEO: Kunihiro Tanaka), has been accepted for the AI for Accelerated Materials Discovery (AI4Mat) Workshop, held in conjunction with Neural Information Processing Systems (NeurIPS) 2025, a world-renowned international conference in the field of AI and machine learning.
\nThe paper will be presented in San Diego, California, USA, on Saturday, December 6, 2025, local time.<\/p>\n
\u3000SAKURA internet Research Center has released a new dataset called MatPROV, which extracts material synthesis procedures from scientific literature and enables the capture of causal relationships among materials, operations, and conditions using directed graphs. The novelty and utility of this dataset were recognized, which led to the acceptance of the paper.<\/p>\n
\u3000SAKURA internet Research Center advances research and development in the field of laboratory informatics, making the management, analysis, and sharing of research data more efficient. Specifically, we are developing an electronic laboratory notebook platform to accelerate the process of scientific discovery. It is extremely important for electronic laboratory notebooks to record experiment procedures accurately and systematically.<\/p>\n
\u3000The paper accepted for the conference focuses on addressing these challenges in the following two ways:
\n1. Researching the optimal capture format for recording experiment procedures as structured data
\n2. Utilizing large language models (LLMs) to automatically convert experiment procedures described in natural language into structured data<\/p>\n
\u3000We selected materials science as our research subject because it is widely known that the procedures of material synthesis greatly affect the resulting material properties. This makes materials science a suitable subject for verifying the effectiveness of digitizing experiment procedures. However, existing studies have relied on limited schemas with predefined structures to capture synthesis procedures and have struggled to capture the complexity observed in real-world material synthesis procedures, such as branching and merging operations.<\/p>\n
\u3000To address these limitations, SAKURA internet Research Center adopted the Provenance Data Model (PROV-DM), an international standard for provenance information, which can flexibly model procedures and causal relationships. Furthermore, we used LLMs to extract synthesis procedures from scientific literature and constructed our own PROV-DM-compliant dataset, MatPROV.<\/p>\n
\u3000MatPROV captures the relationships among materials, operations, and conditions through visually intuitive directed graphs, serving as a foundation broadly applicable to future research, such as AI-driven automated synthesis planning and process optimization.<\/p>\n
\u3000The results of this research are expected to open new possibilities for AI-driven material exploration and to contribute to the acceleration of sustainable material development. Additionally, the knowledge gained from this research can serve as foundational technology for enhancing the recording and structuring of experiment procedures in electronic laboratory notebooks, leading to new developments in data management and utilization within research environments.<\/p>\n
\u3000SAKURA internet Research Center will continue its research and development endeavors to realize new and valuable internet infrastructures for society.<\/p>\n
MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature
\nHirofumi Tsuruta (SAKURA internet Inc.), Masaya Kumagai (SAKURA internet Inc., Kyoto University)<\/p>\n
https:\/\/arxiv.org\/abs\/2509.01042<\/a><\/p>\nOpen Dataset<\/h3>\n
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