用黑客松探索生成式AI在材料科学中的研究与教育应用
Exploring utilization of generative AI for research and education in data-driven materials science
- 通过跨学科协作的黑客松活动，测试生成式AI在科研工具中的辅助作用
- 提出AI助手、图形界面应用等三类具体落地场景
- 适合材料科学与教育领域关注AI融合的研究者参考
生成式AI近期对日常生活、科研与教育均产生深远影响。为探索其在数据驱动材料科学中的高效应用，我们于2024年7月组织了为期一天的黑客松活动——AIMHack2024。来自材料科学、信息科学、生物信息学及凝聚态物理等领域的研究人员共同参与，探讨生成式AI如何促进科研与教学。基于活动成果，本文聚焦三大方向：(1) 开展AI辅助软件试用，(2) 构建软件AI导师，(3) 开发软件图形化界面应用。尽管生成式AI仍在快速演进，本文提供其在数据驱动材料科学中早期应用的实证记录，并总结出将AI融入科研与教育的有效策略。
原文摘要 · Abstract (English)
Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon -- AIMHack2024 -- in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.
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