Large Language Models in Nanoparticles Research: Methods, Applications, Impacts, and Future Directions
TL;DR
The implementation of LLMs across various domains, such as environmental sustainability, drug delivery systems, health surveillance, biomaterials, and tissue engineering, is discussed in this literature review, which also proposes future research directions to guide further advancements in this rapidly evolving field.
Abstract
Large Language Models (LLMs) have emerged as a cutting-edge tool in the era of advanced science and technology, in materials science by enabling novel approaches such as the development of open-source libraries, intelligent research tools, AI accelerated design platforms, AI driven morphology prediction and related innovations. By 2026, a remarkable transition is evident from experimental adoption of artificial intelligence towards its large-scale integration across diverse industries driven by LLMs to understand complex tasks and execute with high accuracy. This review underscores the potential of LLMs in the advancement of nanomaterial and nanoparticle research and positioning them as an alternative-approaches to conventional computational and experimental methods. This study also analyzes and summarizes procedure of LLM-based methodologies in nanomaterial research like automated knowledge extraction, Retrieval-Augmented Generation (RAG), domain specific fine tuning, hypothesis generation and synthesis planning, knowledge graph construction, LLM based agents and research workflows. Moreover, the implementation of LLMs across various domains, such as environmental sustainability, drug delivery systems, health surveillance, biomaterials, and tissue engineering, is discussed in this literature review, which also proposes future research directions to guide further advancements in this rapidly evolving field.