Update README.md

README.md

@@ -23,19 +23,12 @@ This model was presented in the paper [AceFF: A State-of-the-Art Machine Learnin
 
 ## Overview
 
-Acellera AceFF 2.0 is a next-generation **machine learning interatomic potential (MLIP)** designed for **small molecules**. 
+Acellera AceFF-2.0 is a next-generation **machine learning interatomic potential (MLIP)** designed for **small molecules**. 
 It addresses key limitations of traditional molecular mechanics (MM) force fields and earlier NNP models, including restricted atom types, limited charge support, and computational inefficiencies.
 
-The model leverages the TensorNet v2 architecture [1] and the NNP software library TorchMD-Net [2] to provide accurate predictions for diverse drug-like compounds, supporting all key chemical elements and charged molecules. 
-Acellera AceFF 2.0 improves the stability of molecular dynamics simulations, supports 2 fs timesteps, and achieves state-of-the-art accuracy with fewer outliers in RBFE predictions.
-
-
-## Description
-
-Acellera AceFF 2.0 is the second version of a new family of potentials released by [Acellera](https://www.acellera.com). 
-It uses [TensorNet](https://proceedings.neurips.cc/paper_files/paper/2023/file/75c2ec5f98d7b2f50ad68033d2c07086-Paper-Conference.pdf) 2-layers trained 
-on Acellera's internal proprietary dataset of molecular forces and energies using the wB97M-V/def2-tzvppd level of theory and VV10 dispersion corrections.
-
+The model leverages the TensorNet v2 architecture [Farr2026], an evolution of TensorNet [Simeon2024]. 
+It is implemented ubn the MLIP software library TorchMD-Net [Pelaez2024, Farr2026] to provide accurate predictions for diverse drug-like compounds, supporting all key chemical elements and charged molecules. 
+Acellera AceFF 2.0 is the second version of a new family of potentials released by [Acellera](https://www.acellera.com) trained on Acellera's internal proprietary dataset of molecular forces and energies using the wB97M-V/def2-tzvppd level of theory and VV10 dispersion corrections.
 The training set was built on [PubChem](https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF). 
 We extracted the SMILES and generated molecules, filtering out molecules larger than 30 atoms. 
 We kept only molecules with the elements H, B, C, N, O, F, Si, P, S, Cl, Br, and I. 
@@ -57,8 +50,6 @@ The table shows the results on the [Wiggle150](https://pubs.acs.org/doi/10.1021/
 
 *Performance of MLIPs on Wiggle150 benchmark*
 
-More bechmarks coming soon in a pre-print, stay tuned...
-
 ---
 
 ## Key Features
@@ -69,7 +60,6 @@ More bechmarks coming soon in a pre-print, stay tuned...
 - **Integration-Friendly:** Available for RBFE calculations via [HTMD](https://github.com/acellera/htmd).
 - **Open Science:** The model is available with an Apache 2.0 license.
 
-
 ---
 
 ## Usage
@@ -88,7 +78,7 @@ More bechmarks coming soon in a pre-print, stay tuned...
 ## Applications
 
 - **Drug Discovery:** Optimizing lead compounds in hit-to-lead and lead optimization stages using free energy methods.
-- **Binding Free Energy Calculations:** Accurate and efficient RBFE predictions for diverse molecular systems.
+- **Binding Free Energy Calculations:** Accurate and efficient RBFE predictions for diverse molecular systems[Zariquiey2025].
 - **Molecular dynamics:** Capturing higher-body terms than traditional MM force fields, Acellera AceFF can be used for structure minimization and dynamics of small molecules.
 
 ## Limitations
@@ -101,10 +91,10 @@ More bechmarks coming soon in a pre-print, stay tuned...
 
 ## References
 
-[1] Simeon, Guillem, and Gianni De Fabritiis, Tensornet: Cartesian tensor representations for efficient learning of molecular potentials, Advances in Neural Information Processing Systems 36 (2024), https://arxiv.org/abs/2306.06482
+[Simeon2024] Simeon, Guillem, and Gianni De Fabritiis, Tensornet: Cartesian tensor representations for efficient learning of molecular potentials, Advances in Neural Information Processing Systems 36 (2024), https://arxiv.org/abs/2306.06482
 
-[2] Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis, TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations, J. Chem. Theory Comput. 2024, 20, 10, 4076–4087, https://arxiv.org/abs/2402.17660
+[Pelaez2024] Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis, TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations, J. Chem. Theory Comput. 2024, 20, 10, 4076–4087, https://arxiv.org/abs/2402.17660
 
-[3] Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan Doerr, Gianni De Fabritiis, QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials, https://arxiv.org/abs/2501.01811 (2025).
+[Zariquiey2025] Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan Doerr, Gianni De Fabritiis, QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials, https://arxiv.org/abs/2501.01811 (2025).
 
-[4] Stephen E. Farr, Stefan Doerr, Antonio Mirarchi, Francesc Sabanes Zariquiey, Gianni De Fabritiis, AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules, https://arxiv.org/abs/2601.00581
+[Farr2026] Stephen E. Farr, Stefan Doerr, Antonio Mirarchi, Francesc Sabanes Zariquiey, Gianni De Fabritiis, AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules, https://arxiv.org/abs/2601.00581