Add graph-ml pipeline tag (#1)

- Add graph-ml pipeline tag (46933cdef161bef4547e8d148e35b88da222bf6d)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,6 @@
 ---
 license: mit
+pipeline_tag: graph-ml
 tags:
 - mlip
 - transip
@@ -7,6 +8,7 @@ tags:
 - equivariance
 - interatomic
 ---
+
 <div align="center">
 
 # TransIP
@@ -18,11 +20,11 @@ tags:
 
 </div>
 
-
+This repository contains the weights for TransIP, as introduced in [Learning Inter-Atomic Potentials without Explicit Equivariance](https://huggingface.co/papers/2510.00027).
 
 ## GitHub repo
 
-https://github.com/Ahmed-A-A-Elhag/TransIP .
+https://github.com/Ahmed-A-A-Elhag/TransIP
 
 ## FAIR resources
 
@@ -35,21 +37,13 @@ We thank them for making their work publicly available.
 
 ## Acknowledgements
 
-
-This research is partially supported by EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1, EPSRC AI Hub on Mathematical Foundations of Intelligence: An “Erlangen Programme” for AI No. EP/Y028872/1. Further, this research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy (DOE) User Facility, using an AI4Sci@NERSC award (DDR-ERCAP 0034574)
-awarded to AM. S.M.B. acknowledges support from the Center for High Precision Patterning Science (CHiPPS), an Energy Frontier Research Center funded by the U.S. DOE, Office of Science,
-Basic Energy Sciences (BES). AR’s PhD is supported by the Agency for Science Technology and
-Research and the SABS R3 CDT program via the Engineering and Physical Sciences Research
-Council. AR also received compute resources from the DSO National Laboratories - AI Singapore
-(AISG) programme and the Lawrence Livermore National Laboratory. We would like to thank them
-for their resources, which played a significant role in this research. We would also like to thank
-Santiago Vargas, Chaitanya Joshi, and Chen Lin for their fruitful discussions
+This research is partially supported by EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1, EPSRC AI Hub on Mathematical Foundations of Intelligence: An “Erlangen Programme” for AI No. EP/Y028872/1. Further, this research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy (DOE) User Facility, using an AI4Sci@NERSC award (DDR-ERCAP 0034574) awarded to AM. S.M.B. acknowledges support from the Center for High Precision Patterning Science (CHiPPS), an Energy Frontier Research Center funded by the U.S. DOE, Office of Science, Basic Energy Sciences (BES). AR’s PhD is supported by the Agency for Science Technology and Research and the SABS R3 CDT program via the Engineering and Physical Sciences Research Council. AR also received compute resources from the DSO National Laboratories - AI Singapore (AISG) programme and the Lawrence Livermore National Laboratory. We would like to thank them for their resources, which played a significant role in this research. We would also like to thank Santiago Vargas, Chaitanya Joshi, and Chen Lin for their fruitful discussions.
 
 ## Citation
 
 If you use the code of this package or find this work useful, please cite:
-```
 
+```bibtex
 @misc{elhag2026learninginteratomicpotentialsexplicit,
       title={Learning Inter-Atomic Potentials without Explicit Equivariance}, 
       author={Ahmed A. Elhag and Arun Raja and Alex Morehead and Samuel M. Blau and Hongtao Zhao and Christian Tyrchan and Eva Nittinger and Garrett M. Morris and Michael M. Bronstein},