1.5 million materials narratives generated by chatbots
Yang Jeong Park Sung Eun Jerng Jin-Sung Park Choah Kwon Chia-Wei Hsu Zhichu Ren Sungroh Yoon Ju Li
The advent of artificial intelligence (AI) has enabled a comprehensive
exploration of materials for various applications. However, AI models often
prioritize frequently encountered materials in the scientific literature,
limiting the selection of suitable candidates based on inherent physical and
chemical properties. To address this imbalance, we have generated a dataset of
1,494,017 natural language-material paragraphs based on combined OQMD,
Materials Project, JARVIS, COD and AFLOW2 databases, which are dominated by ab
initio calculations and tend to be much more evenly distributed on the periodic
table. The generated text narratives were then polled and scored by both human
experts and ChatGPT-4, based on three rubrics: technical accuracy, language and
structure, and relevance and depth of content, showing similar scores but with
human-scored depth of content being the most lagging. The merger of
multi-modality data sources and large language model (LLM) holds immense
potential for AI frameworks to help the exploration and discovery of
solid-state materials for specific applications.