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Peer-reviewedLicence not statedNature Computational Science

UniFFBench: evaluating universal machine learning force fields against experimental measurements

  • Sajid Mannan1
  • Vaibhav Bihani2
  • Carmelo Gonzales3,4
  • Kin Long Kelvin Lee3,4
  • Nitya Nand Gosvami5
  • Sayan Ranu6,2
  • Santiago Miret7
  • N M Anoop Krishnan8,9
9 affiliations
  1. 1Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi, Hauz Khas, India
  2. 2Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, India
  3. 3Intel Labs, Santa Clara, CA, USA
  4. 4NVIDIA Corporation, Santa Clara, CA, USA
  5. 5Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, India
  6. 6Department of Computer Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, India
  7. 7Intel Labs, Santa Clara, CA, USA. [email protected]
  8. 8Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi, Hauz Khas, India. [email protected]
  9. 9Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, India. [email protected]
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Cite

BibTeXRIS
BibTeX
@article{mannan2026uniffbench,
  title = {{UniFFBench: evaluating universal machine learning force fields against experimental measurements}},
  author = {Sajid Mannan and Vaibhav Bihani and Carmelo Gonzales and Kin Long Kelvin Lee and Nitya Nand Gosvami and Sayan Ranu and Santiago Miret and N M Anoop Krishnan},
  journal = {Nature Computational Science},
  year = {2026},
  month = jul,
  doi = {10.1038/s43588-026-01019-4},
  url = {https://arxiv.org/abs/2508.05762},
}
RIS
TY  - JOUR
TI  - UniFFBench: evaluating universal machine learning force fields against experimental measurements
AU  - Sajid Mannan
AU  - Vaibhav Bihani
AU  - Carmelo Gonzales
AU  - Kin Long Kelvin Lee
AU  - Nitya Nand Gosvami
AU  - Sayan Ranu
AU  - Santiago Miret
AU  - N M Anoop Krishnan
PY  - 2026
DA  - 2026/07/14
JO  - Nature Computational Science
DO  - 10.1038/s43588-026-01019-4
UR  - https://arxiv.org/abs/2508.05762
SN  - 2662-8457
ER  -