FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D

Authors: Feng Chen , Jianguo Huang , Yushan Xiao , Yue Li , Bijun Tang , Xuyu Dong , Xingguo Wang , Feiyang Xie , Jianhui Gu , Dongfeng Cao , Hongda Chen , Yongji Gong , Ruiqi Li , Shuxin Li , Xingji Li , Ruitao Lv , Li Song , Lei Wang , Ye Zhang , Xiaoxu Zhao , Xitao Zheng , Bo An , Zheng Liu

View a PDF of the paper titled FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D, by Feng Chen and 22 other authors

View PDF

HTML (experimental)

Abstract: To accelerate materials discovery, language models must be able to address problems at the research frontier. Yet textbook- and examination-derived benchmarks primarily assess established knowledge, leaving performance on frontier research problems largely untested. We introduce FrontierMat, a benchmark of 330 problems drawn from frontier academic and industrial research in fields including aerospace, semiconductors, embodied intelligence and nuclear fusion. Every problem is validated by leading experimentalists and industry experts in its field. Each problem presents a real study and asks the model to infer a withheld outcome or draw a conclusion from selected experimental evidence. Problems are annotated by functional domain, material system and the scale of the governing mechanism; 130 require cross-scale reasoning. The strongest general-purpose model scores only 53.7%, and on matched problems, cross-scale problems score 8.7 points below single-scale ones. Half of the problems are solved by at most one model, and models fail chiefly by endorsing what is plausible rather than what was observed. A domain-adaptation study with meta-weighted continued pretraining and scale-modular self-distillation raises an open-weight model from 36.7% to 55.4%, matching the strongest proprietary model, with improvements on public materials benchmarks and no decline in aggregate performance on general benchmarks. FrontierMat provides a testbed for measuring progress toward language models that support frontier materials R&D.

Comments:

23 pages, 4 figures, 13 tables

Subjects:

Artificial Intelligence (cs.AI)

ACM classes:

I.2.7; J.2

Cite as:

arXiv:2610.41162 [cs.AI]

(or

arXiv:2610.41162v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41162

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Feng Chen [ view email ]

[v1]

Fri, 30 Oct 2026 02:37:21 UTC (536 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D, by Feng Chen and 22 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.AI

< prev

next >

new

recent

| 2026-10

Change to browse by:

cs

References & Citations

SciX

Google Scholar

Semantic Scholar

export BibTeX citation

Loading...

BibTeX formatted citation

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer ( What is the Explorer? )

Connected Papers Toggle

Connected Papers ( What is Connected Papers? )

Litmaps Toggle

Litmaps ( What is Litmaps? )

scite.ai Toggle

scite Smart Citations ( What are Smart Citations? )

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv ( What is alphaXiv? )

Links to Code Toggle

CatalyzeX Code Finder for Papers ( What is CatalyzeX? )

DagsHub Toggle

DagsHub ( What is DagsHub? )

GotitPub Toggle

Gotit.pub ( What is GotitPub? )

Huggingface Toggle

Hugging Face ( What is Huggingface? )

ScienceCast Toggle

ScienceCast ( What is ScienceCast? )

Demos

Demos

Replicate Toggle

Replicate ( What is Replicate? )

Spaces Toggle

Hugging Face Spaces ( What is Spaces? )

Spaces Toggle

TXYZ.AI ( What is TXYZ.AI? )

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower ( What are Influence Flowers? )

Core recommender toggle

CORE Recommender ( What is CORE? )

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |

Disable MathJax ( What is MathJax? )