Merge pull request #251 from torchmd/guillemsimeon-patch-1

Update README.md
torchmd · Jan 17, 2024 · b1f2f5a · b1f2f5a
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diff --git a/README.md b/README.md
@@ -99,26 +99,30 @@ url={https://openreview.net/forum?id=zNHzqZ9wrRB}
 #### Graph Network 
 
 ```
-@misc{majewski2022machine,
-      title={Machine Learning Coarse-Grained Potentials of Protein Thermodynamics}, 
-      author={Maciej Majewski and Adrià Pérez and Philipp Thölke and Stefan Doerr and Nicholas E. Charron and Toni Giorgino and Brooke E. Husic and Cecilia Clementi and Frank Noé and Gianni De Fabritiis},
-      year={2022},
-      eprint={2212.07492},
-      archivePrefix={arXiv},
-      primaryClass={q-bio.BM}
+@article{Majewski2023,
+  title = {Machine learning coarse-grained potentials of protein thermodynamics},
+  volume = {14},
+  ISSN = {2041-1723},
+  url = {http://dx.doi.org/10.1038/s41467-023-41343-1},
+  DOI = {10.1038/s41467-023-41343-1},
+  number = {1},
+  journal = {Nature Communications},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Majewski,  Maciej and Pérez,  Adrià and Th\"{o}lke,  Philipp and Doerr,  Stefan and Charron,  Nicholas E. and Giorgino,  Toni and Husic,  Brooke E. and Clementi,  Cecilia and Noé,  Frank and De Fabritiis,  Gianni},
+  year = {2023},
+  month = sep 
 }
 ```
 
 #### TensorNet
 
 ```
-@misc{simeon2023tensornet,
-      title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials}, 
-      author={Guillem Simeon and Gianni de Fabritiis},
-      year={2023},
-      eprint={2306.06482},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
+@inproceedings{simeon2023tensornet,
+title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials},
+author={Guillem Simeon and Gianni De Fabritiis},
+booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+year={2023},
+url={https://openreview.net/forum?id=BEHlPdBZ2e}
 }
 ```
 

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -15,40 +15,46 @@ Main reference
 .. code-block:: bibtex
 
     @inproceedings{
-        tholke2021equivariant,
-        title={Equivariant Transformers for Neural Network based Molecular Potentials},
-        author={Philipp Th{\"o}lke and Gianni De Fabritiis},
-        booktitle={International Conference on Learning Representations},
-        year={2022},
-        url={https://openreview.net/forum?id=zNHzqZ9wrRB}
+    tholke2021equivariant,
+    title={Equivariant Transformers for Neural Network based Molecular Potentials},
+    author={Philipp Th{\"o}lke and Gianni De Fabritiis},
+    booktitle={International Conference on Learning Representations},
+    year={2022},
+    url={https://openreview.net/forum?id=zNHzqZ9wrRB}
     }
 
+
 Graph Network
 ~~~~~~~~~~~~~
 
 .. code-block:: bibtex
 
-    @misc{majewski2022machine,
-          title={Machine Learning Coarse-Grained Potentials of Protein Thermodynamics},
-          author={Maciej Majewski and Adrià Pérez and Philipp Thölke and Stefan Doerr and Nicholas E. Charron and Toni Giorgino and Brooke E. Husic and Cecilia Clementi and Frank Noé and Gianni De Fabritiis},
-          year={2022},
-          eprint={2212.07492},
-          archivePrefix={arXiv},
-          primaryClass={q-bio.BM}
-    }
+  @article{Majewski2023,
+  title = {Machine learning coarse-grained potentials of protein thermodynamics},
+  volume = {14},
+  ISSN = {2041-1723},
+  url = {http://dx.doi.org/10.1038/s41467-023-41343-1},
+  DOI = {10.1038/s41467-023-41343-1},
+  number = {1},
+  journal = {Nature Communications},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Majewski,  Maciej and Pérez,  Adrià and Th\"{o}lke,  Philipp and Doerr,  Stefan and Charron,  Nicholas E. and Giorgino,  Toni and Husic,  Brooke E. and   Clementi,  Cecilia and Noé,  Frank and De Fabritiis,  Gianni},
+  year = {2023},
+  month = sep 
+  }
 
 TensorNet
 ~~~~~~~~~
 
 .. code-block:: bibtex
 
-    @misc{simeon2023tensornet,
-          title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials},
-          author={Guillem Simeon and Gianni de Fabritiis},
-          year={2023},
-          eprint={2306.06482},
-          archivePrefix={arXiv},
-          primaryClass={cs.LG}
+    @inproceedings{
+    simeon2023tensornet,
+    title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials},
+    author={Guillem Simeon and Gianni De Fabritiis},
+    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+    year={2023},
+    url={https://openreview.net/forum?id=BEHlPdBZ2e}
     }
 
 

diff --git a/torchmdnet/models/torchmd_et.py b/torchmdnet/models/torchmd_et.py
@@ -16,7 +16,9 @@
 
 
 class TorchMD_ET(nn.Module):
-    r"""The TorchMD equivariant Transformer architecture.
+    r"""Equivariant Transformer's architecture. From
+    Equivariant Transformers for Neural Network based Molecular Potentials; P. Tholke and G. de Fabritiis.
+    ICLR 2022.
 
     This function optionally supports periodic boundary conditions with arbitrary triclinic boxes.
     For a given cutoff, :math:`r_c`, the box vectors :math:`\vec{a},\vec{b},\vec{c}` must satisfy

diff --git a/torchmdnet/models/torchmd_gn.py b/torchmdnet/models/torchmd_gn.py
@@ -16,8 +16,11 @@
 
 
 class TorchMD_GN(nn.Module):
-    r"""The TorchMD Graph Network architecture.
+    r"""Graph Network architecture.
         Code adapted from https://github.com/rusty1s/pytorch_geometric/blob/d7d8e5e2edada182d820bbb1eec5f016f50db1e0/torch_geometric/nn/models/schnet.py#L38
+        and used at
+        Machine learning coarse-grained potentials of protein thermodynamics; M. Majewski et al.
+        Nature Communications (2023)
 
         .. math::
             \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \odot