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Liu et al. J. Mater. Inf. 2025, 5, 27 I http://dx.doi.org/10.20517/jmi.2024.105 Page 15 of 18

cific catalytic simulation tasks while concurrently dropping redundant and outlying data, making it better for
addressing more advanced challenges in iron-based FTS, such as the automatic exploration of surface reaction
pathways, the morphology of FeC x nanoclusters under different chemical environments, or the detailed effects
of alkali promoters in FTS process. Our work may provide a blueprint for utilizing pre-trained LAMs through
fine-tuning methodology in the atomistic simulation not only for iron-based FTS but also for other complex
heterogeneous catalytic systems.

DECLARATIONS

Acknowledgments
The authors acknowledge Dr. Duo Zhang from AI for Science Institute for helping with model training and
dataset visualization and Dr. Yike Huang from AI for Science Institute for sharing template code on utilizing
ASE-GA in surface systems. The authors also appreciate the support of the High-performance Computing
Platform of Peking University and National Supercomputer Center in Tianjin for the computational resources.

Authors’ contributions
Contributed equally to this work: Liu, Z.Q .; Deng, Z.
Dataset preparation, model training and evaluation, data analysis, writing: Liu, Z. Q.; Deng, Z.
Discussion of results, revision: Zhao, H.; Wang, H.; Chen, M.
Project conceptualization, methodology, supervision, revision, funding acquisition: Jiang, H.

Availability of data and materials
The dataset and model used in this study are both available on AIS Square (https://www.aissquare.com/). In
detail, the dataset with the structural information, DFT label information, and DFT input setting files can
be accessed at https://www.aissquare.com/datasets/detail?pageType=datasets&name=FT2DP-dataset-FeCH
O-v1&id=306, and the model with its input scripts is available at https://www.aissquare.com/models/detai
l?pageType=models&id=307. Additionally, the codes for TS optimization are all available in the ATST-Tools
repository (https://github.com/QuantumMisaka/ATST-Tools). All other data and codes supporting the find-
ings presented in this work are available from the corresponding author upon reasonable request.

Financial support and sponsorship
This work is financially supported by the National Key Research and Development Program of China (Project
no. 2022YFB4101401) and National Natural Science Foundation of China (Project no. 22273002).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
©The Author(s) 2025.

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