# Exploring utilization of generative AI for research and education in data-driven materials science

arXiv: https://arxiv.org/abs/2504.08817 · HTML: https://mlsanity.com/paper/2504.08817/

## Key facts

- Cited by 1 papers 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 (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023)
- [Practices and Challenges of Using GitHub Copilot: An Empirical Study](https://mlsanity.com/paper/2303.08733/) (2023)
- [NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science](https://mlsanity.com/paper/2304.13927/) (2023)

## Built on by (most cited first, 1 of 1)

- [Building a research-software catalog with a coding agent: from hackathon prototype to public deployment](https://mlsanity.com/paper/2609.04711/) (2026)

## 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.