Yang Jeong Park, Sung Eun Jerng, Sungroh Yoon, Ju Li
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引用次数: 0
摘要
人工智能(AI)的出现使人们能够全面探索各种应用材料。然而,人工智能模型往往优先考虑科学文献中经常出现的材料实例,从而限制了根据固有物理和化学属性选择合适的候选材料。为了解决这一不平衡问题,我们从 OQMD、Materials Project、JARVIS 和 AFLOW2 数据库中生成了一个由 1,453,493 篇自然语言材料叙述组成的数据集,该数据集基于在整个元素周期表中分布较为均匀的 ab initio 计算结果。然后,人类专家和 GPT-4 根据技术准确性、语言和结构以及内容的相关性和深度三个评分标准对生成的文本叙述进行评分,结果显示得分相近,但人类评分的内容深度最为滞后。多模态数据源与大型语言模型的整合为人工智能框架提供了巨大的潜力,有助于探索和发现固态材料的特定应用。
1.5 million materials narratives generated by chatbots.
The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases based on ab initio calculation results that are more evenly distributed across the periodic table. The generated text narratives were then scored by both human experts and GPT-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 integration of multimodal data sources and large language models holds immense potential for AI frameworks to aid the exploration and discovery of solid-state materials for specific applications of interest.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.