license: cc-by-4.0
language:
- en
tags:
- benchmark
- reproducibility
- computational-materials-science
- agents
pretty_name: AutoMat
size_categories:
- n<1K
extra_gated_prompt: >-
AutoMat is a benchmark of reproducibility claims from computational
materials-science papers. Access is gated to keep the leaderboard meaningful
and to respect upstream paper licensing. Please briefly describe how you
intend to use the dataset.
extra_gated_fields:
Name: text
Affiliation: text
Intended use: text
I agree to use this dataset solely for non-commercial research: checkbox
extra_gated_button_content: Request access
configs:
- config_name: default
data_files:
- split: claims
path: manifest.parquet
AutoMat
AutoMat is a benchmark of reproducibility claims drawn from the computational materials-science literature. Each claim packages a published scientific statement together with the inputs, papers, and reference outputs an autonomous agent (or human) would need to attempt a faithful reproduction.
Layout
Each claim lives under claims/AUTOMAT-XXXX_<author>/ with three subtrees:
| Path | Contents |
|---|---|
meta/provenance.json |
Authoritative metadata (claim ID, paper, DOI, author). |
meta/claim.md |
Human-readable claim statement and reproduction instructions. |
agent_view/ |
What an agent is allowed to see: claim.txt, the paper PDF, input data, custom code. |
reference/ |
Ground-truth reproduction: expected outputs and a reference implementation. |
Coverage
A small number of claims are withheld from the current release at their
authors' request, because the underlying paper is still pending official
publication. These will be added in a later revision once the corresponding
papers are public. The published manifest.parquet lists exactly the claims
included in this release, so any consumer that drives off the manifest stays
in sync automatically.
Using the dataset
This is a gated dataset, so you must be logged into your Hugging Face account (and have been granted access) before downloading. Verify with:
hf auth whoami
If that reports you are not logged in, run:
hf auth login
Once you are logged in, you can download the dataset:
from huggingface_hub import snapshot_download
import pandas as pd
local = snapshot_download(repo_id="jhu-clsp/AutoMat", repo_type="dataset")
manifest = pd.read_parquet(f"{local}/manifest.parquet")
print(manifest.head())
To fetch a single claim only:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="jhu-clsp/AutoMat",
repo_type="dataset",
allow_patterns=["manifest.parquet", "claims/AUTOMAT-0003*"],
)
The companion harness at https://github.com/JHU-CLSP/AutoMat consumes this layout directly.
Evaluation protocol
For each claim, an agent receives the contents of agent_view/ and the
free-form claim text. A successful reproduction is one whose outputs match the
artifacts in reference/expected/ (per the comparison rules described in the
harness harness/evaluators/). Reference implementations under
reference/impl/ are provided for context and to debug failed runs — they are
not intended as the canonical reproduction path.
Licensing notes
- The metadata, claim statements, and reference outputs in this dataset are released under CC-BY-4.0.
- The paper PDFs included under
agent_view/paper/remain the property of their original publishers. Their inclusion here is for reproducibility research under the gated-access terms above; consult each paper's publisher for redistribution rights.
Citation
@article{huang2026automat,
title = {Can Coding Agents Reproduce Findings in
Computational Materials Science?},
author = {Huang, Ziyang and Cao, Yi and
Shargh, Ali K. and Luo, Jing and
Mei, Ruidong and Zaki, Mohd and
Liu, Zhan and Bunstine, Wyatt and
Jurayj, William and Goswami, Somdatta and
McQueen, Tyrel and Shields, Michael and
El-Awady, Jaafar and Clancy, Paulette and
Van Durme, Benjamin and Andrews, Nicholas and
Walden, William and Khashabi, Daniel},
journal = {arXiv preprint arXiv:2605.00803},
year = {2026},
}
Changelog
- v1.0.0 — initial release.