Update README.md

README.md

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 ProtNHF is a generative model for protein sequences that enables continuous, controllable design without retraining. It leverages neural Hamiltonian flows with a Transformer-based energy function to map a latent Gaussian to protein embeddings. Sampling-time bias functions allow steering properties like amino acid composition or net charge smoothly and predictably. Generated sequences achieve high quality as measured by ESM-2 pseudo-perplexity and AlphaFold2 pLDDT scores. ProtNHF provides a flexible, physically interpretable framework for programmable protein sequence generation.
 
+The source code is available here: https://github.com/bharath-raghavan/ProtNHF.git
+
 ## Model Details
 
 This current upload corresponds to model/architecture version 1.
@@ -52,3 +54,18 @@ The training was performed using Pytorch DDP on 64*8 GPUs, with a batch size per
   warmup_epochs: 5
 ```
 
+## Citation
+
+If you use ProtNHF in your research, please cite:
+
+B. Raghavan, and D. M. Rogers  
+**ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation**  
+arXiv:xxxx.xxxxx (2026)
+
+```bibtex
+@article{raghavan2026protnhf,
+  title   = {ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation},
+  author  = {Raghavan, Bharath and Rogers, David M.},
+  journal = {arXiv preprint arXiv:xxxx.xxxxx},
+  year    = {2026}
+}