  Reducing hallucination of Large Language Models (LLMs) is imperative for use
in the sciences, where reliability and reproducibility are 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 hierarchical reasoning-and-acting (ReAct) agents that can
dynamically and recursively interact with computational and experimental data
on Materials Project (MP) and run atomistic simulations via high-throughput
workflow interface. Without fine-tuning, LLaMP demonstrates strong tool usage
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 structure and elastic tensor), and streamline complex tasks in
computational materials and chemistry. We propose a simple metric combining
uncertainty and confidence estimates to evaluate the self-consistency of
responses by LLaMP and vanilla LLMs. Our benchmark shows that LLaMP effectively
mitigates the intrinsic bias in LLMs, counteracting the errors on bulk moduli,
electronic bandgaps, and formation energies that seem to derive from mixed data
sources. We also demonstrate LLaMP's capability to edit crystal structures and
run annealing molecular dynamics simulations using pre-trained machine-learning
force fields. The framework offers an intuitive and nearly hallucination-free
approach to exploring and scaling materials informatics, and establishes a
pathway for knowledge distillation and fine-tuning other language models. Code
and live demo are available at https://github.com/chiang-yuan/llamp
