Authors: Nathaniel H. Park, Tiffany J. Callahan, James L. Hedrick, Tim Erdmann, Sara Capponi
Abstract: Molecular property prediction and generative design via deep learning models
has been the subject of intense research given its potential to accelerate
development of new, high-performance materials. More recently, these workflows
have been significantly augmented with the advent of large language models
(LLMs) and systems of LLM-driven agents capable of utilizing pre-trained models
to make predictions in the context of more complex research tasks. While
effective, there is still room for substantial improvement within the agentic
systems on the retrieval of salient information for material design tasks.
Moreover, alternative uses of predictive deep learning models, such as
leveraging their latent representations to facilitate cross-modal retrieval
augmented generation within agentic systems to enable task-specific materials
design, has remained unexplored. Herein, we demonstrate that large, pre-trained
chemistry foundation models can serve as a basis for enabling semantic
chemistry information retrieval for both small-molecules, complex polymeric
materials, and reactions. Additionally, we show the use of chemistry foundation
models in conjunction with image models such as OpenCLIP facilitate
unprecedented queries and information retrieval across multiple
characterization data domains. Finally, we demonstrate the integration of these
systems within multi-agent systems to facilitate structure and
topological-based natural language queries and information retrieval for
complex research tasks.
Source: http://arxiv.org/abs/2408.11793v1
