Conditional Discrete Diffusion Language Model for Polymer Discovery

This repository contains the core generative modeling pipeline for property-conditioned polymer discovery, leveraging a Conditional Discrete Diffusion Language Model (CDDLM) built on a modern transformer encoder backbone.

Overview

Unlike traditional autoregressive sequence models, this implementation frames molecular generation as a non-autoregressive, iterative denoising process. The model learns to recover clean polymer SMILES structures from completely corrupted (masked) sequences, explicitly guided by continuous physical property constraints.

• Generative Backbone: Utilizes answerdotai/ModernBERT-base as a bidirectional transformer encoder to capture dense, long-range contextual sequence representations across complex macromolecular architectures.
• Property Conditioning: Implements a continuous GaussianFourierProjection embedding module that maps scalar property constraints—such as target electronic band gap (E_g)—into a high-dimensional frequency space to drive the reverse diffusion trajectory.
• Sampling Strategy: Incorporates Classifier-Free Guidance (CFG) during the reverse denoising process, enabling precise control over the trade-off between target property adherence and sequence diversity.
• Validation Pipeline: Features an integrated evaluation suite calculating standard structural metrics (Validity, Uniqueness, Novelty) alongside real-time quantum-chemical verification via a live GFN2-xTB calculator.

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Finetune Results

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Repository Structure

• model.py — Core CDDLM architecture and GaussianFourierProjection embedding layers.
• tokenizer.py — Vocabulary mappings and regex-based tokenization optimized for handling complex polymer branching and wildcards (* / [*]).
• training.py — Main unconditioned/conditioned pre-training script utilizing the PI1M_v2.csv dataset.
• finetune_training.py — Fine-tuning pipeline built for strict conditioning on explicit electronic properties via Egc.csv.
• evaluate_metrics.py — Standardized validation matrix computing internal metrics alongside external quantum-chemical property adherence via the live xTB calculator.
• finetune_inference.ipynb — Interactive workspace for checkpoint evaluation, diverse sample stream generation, and property verification.
• train.sh — Pre-training execution script.
• finetune.sh — Fine-tuning execution script.

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Getting Started

1. Data Requirements

The pipeline expects paths to two primary data tracking files:

• PI1M_v2.csv: Large-scale polymer database used for capturing structural syntax and baseline synthetic accessibility profiles.
• Egc.csv: Target dataset containing explicit SMILES mappings to calculated electronic band gap (E_g) values.

2. Execution

To run baseline pre-training: python training.py (or run: bash train.sh)

To execute property-conditioned fine-tuning for targeted electronic profiles: python finetune_training.py (or run: bash finetune.sh)

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Research Attribution

This codebase is a component of ongoing graduate research at the Georgia Institute of Technology (School of Materials Science & Engineering).

Copyright & Licensing

This project is licensed under the MIT License - see the LICENSE file for details. © 2026 Vansh Suresh Yadav. All rights reserved. This code is intended exclusively for private research evaluation. Copying, distributing, or modifying these files without explicit authorization is strictly prohibited.