AI in Science

AI's Scientific Revolution

AI is becoming a powerful tool for scientific discovery—from predicting protein structures to discovering new materials to analyzing vast datasets.

Key Breakthroughs

AlphaFold (Biology)

Predicts protein structures with atomic accuracy. Released structures for 200 million proteins. Revolutionizing drug discovery and understanding of disease.

GNoME (Materials Science)

Google DeepMind discovered 2.2 million new materials, including 380,000 stable materials for potential use in batteries, solar cells, and computing.

AlphaGeometry (Mathematics)

AI that can solve complex geometry problems at the level of math olympiad gold medalists.

Applications

• Drug discovery — Finding new drug candidates faster
• Climate modeling — Better predictions of climate patterns
• Astronomy — Analyzing telescope data for discoveries
• Particle physics — Processing Large Hadron Collider data
• Genomics — Understanding DNA and genetic diseases

How AI Helps Scientists

• Hypothesis generation — AI suggests new research directions
• Literature review — Summarizing thousands of papers
• Experiment automation — Running and analyzing experiments
• Data analysis — Finding patterns in massive datasets
• Simulation — Modeling complex systems

The AI Scientist

Some are exploring AI systems that can do full scientific research:

• Generate hypotheses
• Design and run experiments
• Analyze results
• Write papers

This is early but progressing rapidly.

Challenges

• Verification — AI predictions still need experimental confirmation
• Interpretability — Understanding why AI made predictions
• Data quality — AI is only as good as its training data
• Credit — How to attribute AI-assisted discoveries