update_readme

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -35,14 +35,39 @@ AceFF 1.1 improves the stability of molecular dynamics simulations, supports 2 f
 
 ## Description
 
-AceFF 1.1 is the first version of a new family of potentials released by [Acellera](https://www.acellera.com). 
+AceFF 1.1 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) 1-layer 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 20 atoms. 
-We kept only molecules with the elements H, B, C, N, O, F, Si, P, S, Cl, Br, and I, and a formal charge of -1,0,1.
-Energy are in eV. Forces are in eV/A.
+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. 
+
+The AceFF1.1 dataset includes an additional 1 million conformations of molecules with up to 30 atoms and more diverse charges, building upon the AceFF1.0 dataset.
+
+---
+
+## Bechmarks
+### Torsion scan
+AceFF1.1 has good results on Seller's et al torsion scan benchmark. Please see [3] for more details.
+
+![torsion scan](figures/torsion_benchmark.png)
+
+### Wiggle150
+The table shows the results on the [Wiggle150](https://pubs.acs.org/doi/10.1021/acs.jctc.5c00015) benchmark. We include AIMNet2 and ANI-2x for comparison.
+
+| **Method** | **MAE (kcal/mol)** | **RMSE (kcal/mol)** |
+|------------|--------------------|---------------------|
+| AceFF1.1   | 2.51               | 3.18                |
+| AceFF1.0   | 2.73               | 3.32                |
+| AIMNet2    | 2.39               | 3.13                |
+| ANI-2X     | 4.41               | 5.41                |
+
+*Performance of NNPs on Wiggle150 benchmark*
+
+### Schrodinger ligands test set
+We create our own hold-out test set by labelling 650 ligands from the [schrodinger public binding free energy benchmark](https://github.com/schrodinger/public_binding_free_energy_benchmark) (Jacs, Merk, and charge_annhil sets) with AceFF DFT level of theory. We evaluate the Force MAE of the AceFF predictions.
+![ligand_testset](figures/ligand_testset.png)
 
 ---
 
@@ -53,7 +78,7 @@ Energy are in eV. Forces are in eV/A.
 - **Improved Stability:** Enables a 2 fs timestep for NNP/MM simulations, significantly reducing computational costs.
 - **Integration-Friendly:** Available for RBFE calculations via [HTMD](https://github.com/acellera/htmd).
 - **Open Science:** The model and all benchmarking data are accessible on GitHub for not-for-profit usage.
-- **Error Metrics:** AceFF 1.1 exhibits lower RMSE and higher Kendall tau correlations compared to traditional MM force fields across various protein targets.[3]
+
 
 ---
 
@@ -61,8 +86,14 @@ Energy are in eV. Forces are in eV/A.
 
 AceFF 1.1 is designed for use alone or in a NNP/MM approach, where the ligand is treated with the neural network potential and the environment with molecular mechanics. 
 
-1. Run ML potential molecular simulations of a small molecule using ACEMD with this [tutorial](https://software.acellera.com/acemd/nnp.html) , e.g. to minimize. 
-1. For a tutorial on running mixed protein-ligand simulations, refer to [NNP/MM in ACEMD](https://software.acellera.com/acemd/nnpmm.html).
+1. [Example notebooks](https://github.com/Acellera/aceff_examples) are available in **Google Colab**, demonstrating the use of AceFF with OpenMM and ASE. 
+
+    - Single point calcuation with ASE [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Acellera/aceff_examples/blob/main/notebooks/aceff_single_point_calculation.ipynb)
+    - ML molecular dynamics of a small molecule with OpenMM [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Acellera/aceff_examples/blob/main/notebooks/aceff_MD_example.ipynb)
+    - MM/ML protein-ligand simulations with OpenMM [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Acellera/aceff_examples/blob/main/notebooks/aceff_protein_ligand.ipynb)
+2. Run ML potential molecular simulations of a small molecule using ACEMD with this [tutorial](https://software.acellera.com/acemd/nnp.html) , e.g. to minimize. 
+3. For a tutorial on running mixed protein-ligand simulations, refer to [NNP/MM in ACEMD](https://software.acellera.com/acemd/nnpmm.html).
+
 
 ---