Update README.md

README.md

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 # CGSchNet: Coarse-Grained SchNet Model
 
-Pre-trained coarse-grained SchNet neural network force field for molecular dynamics simulation of proteins.
+Pre-trained coarse-grained SchNet neural network force field for molecular dynamics simulation of proteins. Exactly copied from CGSchNet [Charron et. al. 2025].
 
 ## Model
 
@@ -19,7 +19,7 @@ Pre-trained coarse-grained SchNet neural network force field for molecular dynam
 
 ## Initial Configurations
 
-Batched initial configurations (batch size 32) for each protein, located in `configurations/`:
+Initial configurations for each protein, located in `configurations/`:
 
 | Protein | File | Description |
 |---------|------|-------------|
@@ -57,7 +57,7 @@ configs = download_file("pingzhili/cg-schnet", "configurations/1enh_configuratio
 ## References
 
 **Transferable coarse-grained model:**
-> Li, P., Chen, Y., Musil, F., et al. Transferable coarse-grained models of proteins by graph neural network and automatic differentiation. *Nature Chemistry* (2025). https://doi.org/10.1038/s41557-025-01874-0
+> Charron, N.E., Bonneau, K., Pasos-Trejo, A.S. et al. Navigating protein landscapes with a machine-learned transferable coarse-grained model. Nat. Chem. 17, 1284–1292 (2025). https://doi.org/10.1038/s41557-025-01874-0.
 
 **FlashMD acceleration:**
 > Li, P., Li, H., Liu, Z., Lin, X., & Chen, T. FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics. *arXiv:2602.13140* (2026). https://arxiv.org/abs/2602.13140