文章信息/Info

Title:

Research Progress of Large Language Models in the Field of Materials Science

文章編號:

1674-3962(2025)05-0409-15

作者:

閆姿霓; 饒梓元; 曾小勤

1. 上海交通大學(xué) 輕合金精密成型國家研究中心，上海 200240 2. 上海交通大學(xué) 金屬基復(fù)合材料國家重點(diǎn)實驗室，上海 200240

Author(s):

YAN Zini; RAO Ziyuan; ZENG Xiaoqin

1.National Engineering Research Center of Light Alloy Net Forming，Shanghai Jiao Tong University, Shanghai 200240，China 2.State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China

關(guān)鍵詞:

大語言模型; 自然語言處理; 機(jī)器學(xué)習(xí); 人工智能; 可解釋性; 材料科學(xué)

Keywords:

large language model;  natural language processing;  machine learning;  artificial intelligence;  interpretability;  materials science

分類號:

TB30; TP18

DOI:

10.7502/j.issn.1674-3962.202409019

文獻(xiàn)標(biāo)志碼:

A

摘要:

數(shù)據(jù)驅(qū)動方法已成為材料研究的重要趨勢，大語言模型的出現(xiàn)不僅為材料科學(xué)領(lǐng)域非結(jié)構(gòu)化數(shù)據(jù)的處理提供了潛在的解決方案，還能夠通過其強(qiáng)大的語言理解和生成能力，助力科研中的知識發(fā)現(xiàn)、自動化分析、可解釋性提升以及多模塊協(xié)同操作，從而可推動材料科學(xué)研究的效率提升與創(chuàng)新突破。從大語言模型的理論基礎(chǔ)出發(fā)，探討其重要功能、優(yōu)化方法及其在材料科學(xué)中的應(yīng)用。特別是，大語言模型能夠有效處理和提取非結(jié)構(gòu)化文本中的關(guān)鍵信息，幫助加速材料的發(fā)現(xiàn)與設(shè)計。最后還展望了大語言模型在人工智能與材料科學(xué)融合領(lǐng)域的未來發(fā)展前景，指出其在推動材料科學(xué)研究自動化和智能化方面的巨大潛力。

Abstract:

Data-driven approaches have become an important trend in materials research. The emergence of large language models not only provides a potential solution for unstructured data processing in the field of materials science, but also, through their powerful language understanding and generation capabilities, facilitates knowledge discovery, automated analysis, improved interpretability, and multi-module collaborative operations, thus enhancing research efficiency and driving breakthroughs in materials science. This article explores the theoretical foundations of large language models, examines their key functions, optimization methods, and applications in materials science. In particular, large language models can effectively process and extract key information from unstructured texts, helping to accelerate material discovery and design. This article also envisions the future development of large language models in the integration of artificial intelligence and materials science, highlighting their significant potential in advancing the automation and intelligence of materials research.