Update ReadMe

README.md

@@ -1,6 +1,22 @@
 ---
 license: apache-2.0
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: feature-extraction
+tags:
+- chemistry
+- foundation models
+- AI4Science
+- materials
+- molecules
 ---
+# Introduction to IBM's Foundation Models for Materials
+
+Welcome to IBM's series of large foundation models for sustainable materials. Our models span a variety of representations and modalities, including SMILES, SELFIES, 3D atom positions, 3D density grids, molecular graphs, and other formats. These models are designed to support and advance research in materials science and chemistry.
+
+GitHub: [GitHub Link](https://github.com/IBM/materials/tree/main)
+
 # SMILES-based Transformer Encoder-Decoder (SMI-TED)
 
 This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
@@ -133,4 +149,4 @@ For decoder, you can use the function, so you can return from embeddings to SMIL
 ```python
 with torch.no_grad():
     decoded_smiles = model.decode(encoded_embeddings)
-```
+```