title: Reflections from the 2024 Large Language Model (LLM) Hackathon for
    Applications in Materials Science and Chemistry
summary: 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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paper_authors.name: Gabriel Vogel
paper_authors._id: 6746253711a5bef3f283bfbd
paper_authors.name: Christoph Völker
paper_authors._id: 6746253711a5bef3f283bfbe
paper_authors.name: Jan Weinreich
paper_authors._id: 6746253711a5bef3f283bfbf
paper_authors.name: Faradawn Yang
paper_authors._id: 6746253711a5bef3f283bfc0
paper_authors.name: Mohd Zaki
paper_authors._id: 6746253711a5bef3f283bfc1
paper_authors.name: Chi Zhang
paper_authors._id: 6746253711a5bef3f283bfc2
paper_authors.name: Sylvester Zhang
paper_authors._id: 6746253711a5bef3f283bfc3
paper_authors.name: Weijie Zhang
paper_authors._id: 6746253711a5bef3f283bfc4
paper_authors.name: Ruijie Zhu
paper_authors._id: 6746253711a5bef3f283bfc5
paper_authors.name: Shang Zhu
paper_authors._id: 6746253711a5bef3f283bfc6
paper_authors.name: Jan Janssen
paper_authors._id: 6746253711a5bef3f283bfc7
paper_authors.name: Ian Foster
paper_authors._id: 6746253711a5bef3f283bfc8
paper_authors.name: Ben Blaiszik
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-11-26T19:44:58.475Z
paper_authors._id: 61843f352d33f05d928f9ec4
paper_authors.avatarUrl: /avatars/e23852e2e3c34c7dcd54225e29890458.svg
paper_authors.fullname: Ben Blaiszik
paper_authors.type: user
paper_authors.user: blaiszik
paper_title: Reflections from the 2024 Large Language Model (LLM) Hackathon for
    Applications in Materials Science and Chemistry
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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