Skip to content

Project Motivation

Problem

Advances in chemistry and materials science increasingly depend on the ability to extract structured data from the vast body of scientific literature. However, much of this information remains embedded in unstructured formats — such as free text, complex tables, and visual figures — making it difficult to reuse for computational analysis.

Manual data extraction is:

  • Labor-intensive and slow
  • Prone to inconsistencies and human error
  • Unscalable for the growing volume of publications

Traditional NLP tools, often trained on general or biomedical corpora, struggle with the domain-specific syntax and semantics of chemistry. Moreover, most existing tools are text-focused and cannot access information presented in other modalities like chemical diagrams, plots, and structured tables, which are critical in chemical reporting.

Relevance

This is a central challenge for the research community because:

  • Reliable machine learning models require structured, domain-specific data
  • Scientific progress is limited by the speed and accuracy of data curation
  • Multimodal content — a hallmark of chemistry publications — requires models that can interpret and align information across text, tables, and images

Recent progress in large language models (LLMs) and multimodal transformers has shown potential, but these models often underperform in chemical contexts due to:

  • Lack of fine-grained benchmark datasets
  • Inadequate multimodal training data
  • The absence of standardized evaluation protocols

Goals and Objectives

The central goal of ChemX is to create a comprehensive, expert-validated benchmark for chemical information extraction, enabling the development and assessment of AI systems across multiple chemical domains.

To accomplish this, the project aims to:

  • ✅ Collect and annotate 10 datasets from published chemical literature
  • ✅ Capture multimodal representations — including text, tables, figures, and chemical structures
  • ✅ Apply rigorous expert validation to ensure annotation quality and consistency
  • ✅ Establish standardized evaluation metrics for benchmarking model performance
  • ✅ Support reproducibility and transparency through detailed documentation and metadata

By addressing the lack of multimodal benchmarks in chemistry, ChemX provides a foundation for robust, scalable, and trustworthy AI tools that can transform scientific discovery in chemistry and materials science.