Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials

TL;DR

This paper develops TopoChat, a specialized dialogue system for topological materials by integrating a material knowledge graph with LLMs, significantly improving domain-specific information retrieval and reasoning capabilities.

Contribution

The paper introduces TopoChat, a novel domain-specific LLM-based dialogue system for topological materials, leveraging a material knowledge graph and prompt learning to enhance performance.

Findings

TopoChat outperforms naive LLMs in material querying and reasoning.

The system enables efficient, accurate retrieval of topological material information.

It facilitates knowledge interaction and supports research in condensed matter physics.

Abstract

Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance in the text generation task, showing the ability to understand and respond to complex instructions. However, the performance of naive LLMs in speciffc domains is limited due to the scarcity of domain-speciffc corpora and specialized training. Moreover, training a specialized large-scale model necessitates signiffcant hardware resources, which restricts researchers from leveraging such models to drive advances. Hence, it is crucial to further improve and optimize LLMs to meet speciffc domain demands and enhance their scalability. Based on the condensed matter data center, we establish a material knowledge graph (MaterialsKG) and integrate it with literature. Using large language models and prompt learning, we develop a specialized dialogue system for topological materials called TopoChat. Compared to naive LLMs, TopoChat exhibits superior performance in structural and property querying, material recommendation, and complex relational reasoning. This system enables efffcient and precise retrieval of information and facilitates knowledge interaction, thereby encouraging the advancement on the ffeld of condensed matter materials.