This research applies natural language processing techniques to chemical reaction data, treating chemical structures as a specialized language to predict reaction yields with higher accuracy.

• Adapts ULMFiT (Universal Language Model Fine Tuning) specifically for chemical reaction yield prediction
• Addresses key challenges of imbalance and sparsity in chemical reaction datasets
• Transforms complex chemical structures into language-based representations for improved processing
• Offers a more efficient approach than traditional chemical prediction models

For chemical engineering professionals, this innovation enables better yield predictions leading to optimized manufacturing processes, reduced waste, and potentially significant cost savings in chemical production.

Efficient Machine Learning Approach for Yield Prediction in Chemical Reactions