Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN
- DOI
- 10.1007/s11227-025-07029-9
- Published
- 2025-03-14
- Container
- The Journal of Supercomputing
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s11227-025-07029-9,
title = {Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN},
author = {Massimiliano Lupo Pasini and Jong Youl Choi and Kshitij Mehta and Pei Zhang and David Rogers and Jonghyun Bae and Khaled Z. Ibrahim and Ashwin M. Aji and Karl W. Schulz and Jordà Polo and Prasanna Balaprakash},
year = {2025},
journal = {The Journal of Supercomputing},
doi = {10.1007/s11227-025-07029-9},
url = {https://doi.org/10.1007/s11227-025-07029-9}
}RIS
TY - JOUR TI - Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN AU - Massimiliano Lupo Pasini AU - Jong Youl Choi AU - Kshitij Mehta AU - Pei Zhang AU - David Rogers AU - Jonghyun Bae AU - Khaled Z. Ibrahim AU - Ashwin M. Aji AU - Karl W. Schulz AU - Jordà Polo AU - Prasanna Balaprakash PY - 2025 JO - The Journal of Supercomputing DO - 10.1007/s11227-025-07029-9 UR - https://doi.org/10.1007/s11227-025-07029-9 ER -
APA
Pasini, M. L., Choi, J. Y., Mehta, K., Zhang, P., Rogers, D., Bae, J., Ibrahim, K. Z., Aji, A. M., Schulz, K. W., Polo, J., & Balaprakash, P. (2025). Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN. The Journal of Supercomputing. https://doi.org/10.1007/s11227-025-07029-9
Source records
- crossref · retrieved 2026-09-25T12:08:56.440Z