Generative retrieval-augmented ontologic graph and multi-agent
  strategies for interpretive large language model-based materials design
Markus J. Buehler
Transformer neural networks show promising capabilities, in particular for
uses in materials analysis, design and manufacturing, including their capacity
to work effectively with both human language, symbols, code, and numerical
data. Here we explore the use of large language models (LLMs) as a tool that
can support engineering analysis of materials, applied to retrieving key
information about subject areas, developing research hypotheses, discovery of
mechanistic relationships across disparate areas of knowledge, and writing and
executing simulation codes for active knowledge generation based on physical
ground truths. When used as sets of AI agents with specific features,
capabilities, and instructions, LLMs can provide powerful problem solution
strategies for applications in analysis and design problems. Our experiments
focus on using a fine-tuned model, MechGPT, developed based on training data in
the mechanics of materials domain. We first affirm how finetuning endows LLMs
with reasonable understanding of domain knowledge. However, when queried
outside the context of learned matter, LLMs can have difficulty to recall
correct information. We show how this can be addressed using
retrieval-augmented Ontological Knowledge Graph strategies that discern how the
model understands what concepts are important and how they are related.
Illustrated for a use case of relating distinct areas of knowledge - here,
music and proteins - such strategies can also provide an interpretable graph
structure with rich information at the node, edge and subgraph level. We
discuss nonlinear sampling strategies and agent-based modeling applied to
complex question answering, code generation and execution in the context of
automated force field development from actively learned Density Functional
Theory (DFT) modeling, and data analysis.