LLaMP: Large Language Model Made Powerful for High-fidelity Materials
  Knowledge Retrieval and Distillation
Yuan Chiang Chia-Hong Chou Janosh Riebesell
Reducing hallucination of Large Language Models (LLMs) is imperative for use
in the sciences where reproducibility is crucial. However, LLMs inherently lack
long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to
fine-tune them on domain-specific literature and data. Here we introduce LLaMP,
a multimodal retrieval-augmented generation (RAG) framework of multiple
data-aware reasoning-and-acting (ReAct) agents that dynamically interact with
computational and experimental data on Materials Project (MP). Without
fine-tuning, LLaMP demonstrates an ability to comprehend and integrate various
modalities of materials science concepts, fetch relevant data stores on the
fly, process higher-order data (such as crystal structures and elastic
tensors), and summarize multi-step procedures for solid-state synthesis. We
show that LLaMP effectively corrects errors in GPT-3.5's intrinsic knowledge,
reducing a 5.21% MAPE on frequently-documented bandgaps and a significant
1103.54% MAPE on formation energies -- errors that GPT-3.5 seems to derive from
mixed data sources. Additionally, LLaMP substantially reduces the hallucinated
volumetric strain in a diamond cubic silicon structure from 66.3% to 0. The
proposed framework offers an intuitive and nearly hallucination-free approach
to exploring materials informatics and establishes a pathway for knowledge
distillation and fine-tuning other language models. We envision the framework
as a valuable component for scientific hypotheses and a foundation for future
autonomous laboratories where multiple LLM agents communicate and cooperate
with robotics to drive material synthesis and chemical reactions without
hard-coded human logic and intervention.