Exploring utilization of generative AI for research and education in data-driven materials science
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 9 Apr 2025.
Builds on
6 of 30 references.
References · 6
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
- 2023Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPTcited by 71
- 2023Practices and Challenges of Using GitHub Copilot: An Empirical Studycited by 14
- 2023NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials sciencecited by 3
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Citing papers per year: 2026: 1.
1 paper.