Reflections from the 2024 Large Language Model (LLM) Hackathon for
  Applications in Materials Science and Chemistry
Here, we present the outcomes from the second Large Language Model (LLM)
Hackathon for Applications in Materials Science and Chemistry, which engaged
participants across global hybrid locations, resulting in 34 team submissions.
The submissions spanned seven key application areas and demonstrated the
diverse utility of LLMs for applications in (1) molecular and material property
prediction; (2) molecular and material design; (3) automation and novel
interfaces; (4) scientific communication and education; (5) research data
management and automation; (6) hypothesis generation and evaluation; and (7)
knowledge extraction and reasoning from scientific literature. Each team
submission is presented in a summary table with links to the code and as brief
papers in the appendix. Beyond team results, we discuss the hackathon event and
its hybrid format, which included physical hubs in Toronto, Montreal, San
Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable
local and virtual collaboration. Overall, the event highlighted significant
improvements in LLM capabilities since the previous year's hackathon,
suggesting continued expansion of LLMs for applications in materials science
and chemistry research. These outcomes demonstrate the dual utility of LLMs as
both multipurpose models for diverse machine learning tasks and platforms for
rapid prototyping custom applications in scientific research.
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Reflections from the 2024 Large Language Model (LLM) Hackathon for
  Applications in Materials Science and Chemistry
Here, we present the outcomes from the second Large Language Model (LLM)
Hackathon for Applications in Materials Science and Chemistry, which engaged
participants across global hybrid locations, resulting in 34 team submissions.
The submissions spanned seven key application areas and demonstrated the
diverse utility of LLMs for applications in (1) molecular and material property
prediction; (2) molecular and material design; (3) automation and novel
interfaces; (4) scientific communication and education; (5) research data
management and automation; (6) hypothesis generation and evaluation; and (7)
knowledge extraction and reasoning from scientific literature. Each team
submission is presented in a summary table with links to the code and as brief
papers in the appendix. Beyond team results, we discuss the hackathon event and
its hybrid format, which included physical hubs in Toronto, Montreal, San
Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable
local and virtual collaboration. Overall, the event highlighted significant
improvements in LLM capabilities since the previous year's hackathon,
suggesting continued expansion of LLMs for applications in materials science
and chemistry research. These outcomes demonstrate the dual utility of LLMs as
both multipurpose models for diverse machine learning tasks and platforms for
rapid prototyping custom applications in scientific research.
The hackathon showcased the diverse applications of large language models in materials science and chemistry, highlighting improvements in prediction, design, automation, communication, data management, hypothesis evaluation, and knowledge extraction.
Large Language Model LLM molecular property prediction material property prediction molecular design material design automation interfaces scientific communication education research data management hypothesis generation evaluation knowledge extraction reasoning scientific literature hackathon hybrid format physical hubs global online hub rapid prototyping