materials.smi ted EU AI Act Compliance Profile

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

Paper (pre-print): arXiv:2407.20267

Paper: Communications Chemistry

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".

Paper: Arxiv Link

We provide the model weights in two formats:

• PyTorch (.pt): smi-ted-Light_40.pt
• safetensors (.safetensors): model_weights.safetensors

For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.

Introduction

We present a large encoder-decoder chemical foundation model, SMILES-based Transformer Encoder-Decoder (SMI-TED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants (289M and 8X289M). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: eduardo.soares@ibm.com or evital@br.ibm.com.

Table of Contents

1. Getting Started
   1. Pretrained Models and Training Logs
   2. Replicating Conda Environment
2. [Pretraining](#pretr

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