Chapter 9: Multimodal Generative AI for Sciences

Introduction: Beyond Single-Modality AI

The Road to Multimodal AI: A Progressive View

Era 1: Transfer Learning from Natural Images (2012–2018)

Era 2: Domain-Specific Self-Supervised Learning (2019–2022)

Era 3: Vision-Language Alignment (2021–Present)

CONCH: A Concrete Example

Why This Progression Matters

Part I: Vision-Language Models for Science

The Challenge of Scientific Images

Scientific Image-Text Model

Zero-Shot Scientific Image Classification

Visual Question Answering for Lab Images

Part II: Graph-Text Models for Molecules

Molecular Graphs as Structured Data

Multimodal Molecular Model

Applications: Text-Based Molecular Retrieval

Part III: Time Series with Textual Context

Contextualizing Sensor Data

Multimodal Time Series Model

Validating Multimodal Learning: Ablation Studies

Designing Tasks with Cross-Modal Dependencies

Part IV: Multimodal Fusion Architectures

Cross-Modal Attention Mechanisms

Early vs Late vs Attention Fusion

Part V: Scientific Document Understanding

Extracting Knowledge from Papers

Part VI: Training Multimodal Scientific Models

Self-Supervised Pretraining

Domain-Specific Fine-Tuning

Part VII: Practical Applications

Application 1: Automated Lab Documentation

Application 2: Cross-Modal Molecular Search

Application 3: Text-Conditional Molecule Generation

Laboratory Automation with Multimodal Integration

Summary

References

Additional Resources