Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by Yoel Zimmermann, Adib Bazgir, Zartashia Afzal, Fariha Agbere, Qianxiang Ai, Nawaf Alampara, Alexander Al-Feghali, Mehrad Ansari, Dmytro Antypov, Amro Aswad, Jiaru Bai, Viktoriia Baibakova, Devi Dutta Biswajeet, Erik Bitzek, Joshua D. Bocarsly, Anna Borisova, Andres M Bran, L. Catherine Brinson, Marcel Moran Calderon, Alessandro Canalicchio, Victor Chen, Yuan Chiang, Defne Circi, Benjamin Charmes, Vikrant Chaudhary, Zizhang Chen, Min-Hsueh Chiu, Judith Clymo, Kedar Dabhadkar, Nathan Daelman, Archit Datar, Wibe A. de Jong, Matthew L. Evans, Maryam Ghazizade Fard, Giuseppe Fisicaro, Abhijeet Sadashiv Gangan, Janine George, Jose D. Cojal Gonzalez, Michael Götte, Ankur K. Gupta, Hassan Harb, Pengyu Hong, Abdelrahman Ibrahim, Ahmed Ilyas, Alishba Imran, Kevin Ishimwe, Ramsey Issa, Kevin Maik Jablonka, Colin Jones, Tyler R. Josephson, Greg Juhasz, Sarthak Kapoor, Rongda Kang, Ghazal Khalighinejad, Sartaaj Khan, Sascha Klawohn, Suneel Kuman, Alvin Noe Ladines, Sarom Leang, Magdalena Lederbauer, Sheng-Lun, Liao, Hao Liu, Xuefeng Liu, Stanley Lo, Sandeep Madireddy, Piyush Ranjan Maharana, Shagun Maheshwari, Soroush Mahjoubi, José A. Márquez, Rob Mills, Trupti Mohanty, Bernadette Mohr, Seyed Mohamad Moosavi, Alexander Moßhammer, Amirhossein D. Naghdi, Aakash Naik, Oleksandr Narykov, Hampus Näsström, Xuan Vu Nguyen, Xinyi Ni, Dana O’Connor, Teslim Olayiwola, Federico Ottomano, Aleyna Beste Ozhan, Sebastian Pagel, Chiku Parida, Jaehee Park, Vraj Patel, Elena Patyukova, Martin Hoffmann Petersen, Luis Pinto, José M. Pizarro, Dieter Plessers, Tapashree Pradhan, Utkarsh Pratiush, Charishma Puli, Andrew Qin, Mahyar Rajabi, Francesco Ricci
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); Chemical Physics (physics.chem-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the results from the second Large Language Model (LLM) Hackathon, a global event that brought together 34 teams to apply LLMs in materials science and chemistry. The submissions span seven key areas, including molecular property prediction, material design, automation, scientific communication, research data management, hypothesis generation, and knowledge extraction. Each team submission is summarized in a table with links to the code and brief papers in the appendix. Beyond the team results, the paper discusses the hackathon event and its hybrid format, featuring physical hubs in six cities and an online hub for global collaboration. The event showcases significant improvements in LLM capabilities since the previous year’s hackathon, indicating continued expansion of LLMs for applications in materials science and chemistry research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a big competition where people used special computer models to help with problems in chemistry and materials science. Thirty-four teams worked together to solve different tasks, like predicting the properties of molecules or designing new materials. The teams did things like make robots that can do tasks on their own, create new ways to communicate scientific ideas, and even help find answers to scientific questions. The event was special because it brought people from all over the world together in a mix of online and in-person meetings. This paper shows how these computer models are getting better at helping with these problems, which is exciting for scientists. |
Keywords
* Artificial intelligence * Large language model
