Applications of natural language processing and large language models in materials discovery

The transformative impact of artificial intelligence (AI) technologies on materials science has revolutionized the study of materials problems. By leveraging well-characterized datasets derived from the scientific literature, AI-powered tools such as Natural Language Processing (NLP) have opened new avenues to accelerate materials research. The advances in NLP techniques and the development of large language models (LLMs) facilitate the efficient extraction and utilization of information. This review explores the application of NLP tools in materials science, focusing on automatic data extraction, materials discovery, and autonomous research. We also discuss the challenges and opportunities associated with utilizing LLMs and outline the prospects and advancements that will propel the field forward.

Authors

Institutions

Publication Details

Journal
npj Computational Materials
Published
2025-03-24
DOI
https://doi.org/10.1038/s41524-025-01554-0
Citations
75
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
38.84
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Applications of natural language processing and large language models in materials discovery

Shaohan Tian, Hao Wang, Yanjing Su, Turab Lookman et al.
75 citations
npj Computational Materials
Machine Learning in Materials Science
38.84
article

Applications of natural language processing and large language models in materials discovery

Shaohan Tian, Hao Wang, Yanjing Su, Turab Lookman, Xue Jiang, Weiren Wang
article en
75 citations

Abstract

The transformative impact of artificial intelligence (AI) technologies on materials science has revolutionized the study of materials problems. By leveraging well-characterized datasets derived from the scientific literature, AI-powered tools such as Natural Language Processing (NLP) have opened new avenues to accelerate materials research. The advances in NLP techniques and the development of large language models (LLMs) facilitate the efficient extraction and utilization of information. This review explores the application of NLP tools in materials science, focusing on automatic data extraction, materials discovery, and autonomous research. We also discuss the challenges and opportunities associated with utilizing LLMs and outline the prospects and advancements that will propel the field forward.

npj Computational MaterialsVol. 11(1)
Mayaterials (United States) (US), Liaoning Academy of Materials, University of Science and Technology Beijing (CN)
Quality Education
Openalex Percentile: Top 0%
Machine Learning in Materials Science
38.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.