Interview Between Markus J. Buehler and Tomás Saraceno

Buehler, Markus J.; Saraceno, Tomás

doi:10.7551/mitpress/11236.003.0009

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LLMs are particularly intriguing due to their ability to reason and to apply knowledge recall, synthesis, and translation across multitudes of domains from logic to mathematics to simulation data. [ 31,65–69 ]…”

Section: Discussionmentioning

confidence: 99%

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio‐Inspired Materials

Luu,

Buehler

2023

Advanced Science

Self Cite

View full text Add to dashboard Cite

The study of biological materials and bio‐inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open‐source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer‐reviewed articles in the field of structural biological and bio‐inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval‐Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio‐inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

show abstract

Section: Discussionmentioning

confidence: 99%

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio‐Inspired Materials

Luu,

Buehler

2023

Advanced Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, transformer architectures, which exhibit outstanding performance in natural language processing (NLP), have gained attention for their performance . Transformers can discern importance through a self-attention mechanism and achieve superior performance by gaining a deeper understanding of the context. , Furthermore, these models have facilitated agent-based modeling, aiding in the automation of problem-solving in the field of materials. − These architectures require a pretraining process during which they learn general characteristics from extensive data sets. When fine-tuned for a specific task, they are known to perform better, even with limited data .…”

Section: Introductionmentioning

confidence: 99%

Multimodal Transformer for Property Prediction in Polymers

Han,

Kang,

Park

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

In this work, we designed a multimodal transformer that combines both the Simplified Molecular Input Line Entry System (SMILES) and molecular graph representations to enhance the prediction of polymer properties. Three models with different embeddings (SMILES, SMILES + monomer, and SMILES + dimer) were employed to assess the performance of incorporating multimodal features into transformer architectures. Fine-tuning results across five properties (i.e., density, glass-transition temperature (T g ), melting temperature (T m ), volume resistivity, and conductivity) demonstrated that the multimodal transformer with both the SMILES and the dimer configuration as inputs outperformed the transformer using only SMILES across all five properties. Furthermore, our model facilitates in-depth analysis by examining attention scores, providing deeper insights into the relationship between the deep learning model and the polymer attributes. We believe that our work, shedding light on the potential of multimodal transformers in predicting polymer properties, paves a new direction for understanding and refining polymer properties.

show abstract

Interview Between Markus J. Buehler and Tomás Saraceno

Cited by 2 publications

References 0 publications

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio‐Inspired Materials

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio‐Inspired Materials

Multimodal Transformer for Property Prediction in Polymers

Contact Info

Product

Resources

About