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DTSTART;TZID=Europe/Stockholm:20240604T113000
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UID:submissions.pasc-conference.org_PASC24_sess146_msa223@linklings.com
SUMMARY:Large Language Models and Agentic Systems for Bio-Inspired Materia
 ls Design
DESCRIPTION:Minisymposium\n\nRachel Luu and Markus Buehler (Massachusetts 
 Institute of Technology)\n\nFrom seashells to mammal hooves to plant stems
 , biological materials have long captivated materials scientists and mecha
 nical engineers due to their impressive hierarchical structure-property re
 lationships. By understanding biological insights and motifs, the design o
 f bio-inspired materials is empowered and poised to benefit a diverse rang
 e of applications, including sustainability. Modern generative AI framewor
 ks, especially large language models (LLMs), show remarkable potential for
  science-focused applications, excelling notably in the study of biologica
 l materials through the utilization of rich legacy literature. We present 
 BioinspiredLLM, an open-source conversational large language model that wa
 s finetuned on a corpus of biological materials literature. The model show
 s strong abilities in knowledge recall, creative hypothesis generation, an
 d seamless integration into multi-agent systems. Multi-agent/agentic syste
 ms facilitate the interaction of multiple advanced AI systems, thereby exp
 anding the scope of knowledge, enhancing data retrieval capabilities, and 
 fostering critical thinking. This approach is demonstrated through multipl
 e bio-inspired materials design scenarios.\n\nDomain: Chemistry and Materi
 als\n\nSession Chair: John Gounley (Oak Ridge National Laboratory)
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