Open Access

REVIEW

Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials

Ying Shen¹, Shichao Zhao¹, Yanfei Lv¹, Fei Chen¹, Li Fu^1,*, Hassan Karimi-Maleh^2,*

1 College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
2 The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, 324000, China

* Corresponding Authors: Li Fu. Email: ; Hassan Karimi-Maleh. Email:

(This article belongs to the Special Issue: Computational Analysis of Micro-Nano Material Mechanics and Manufacturing)

Computers, Materials & Continua 2025, 84(2), 1921-1950. https://doi.org/10.32604/cmc.2025.067427

Received 03 May 2025; Accepted 12 June 2025; Issue published 03 July 2025

Abstract

This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature, text-based property prediction, hypothesis generation, synthesis planning, and knowledge graph construction. We comparatively analyze leading LLMs and domain-specific frameworks (e.g., CatBERTa, CataLM, CatGPT) in terms of methodology, application scope, performance metrics, and limitations. Through curated case studies across key electrocatalytic reactions—HER, OER, ORR, and CO₂RR—we highlight emerging trends such as the growing use of embedding-based prediction, retrieval-augmented generation, and fine-tuned scientific LLMs. The review also identifies persistent challenges, including data heterogeneity, hallucination risks, lack of standard benchmarks, and limited multimodal integration. Importantly, we articulate future research directions, such as the development of multimodal and physics-informed MatSci-LLMs, enhanced interpretability tools, and the integration of LLMs with self-driving laboratories for autonomous discovery. By consolidating fragmented advances and outlining a unified research roadmap, this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.

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Keywords

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