Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery

Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics.

How ERA Supports Scientific Coding

• ERA starts with a scientific problem and a success metric.
• It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions.
• Its tree-search process evaluates thousands of alternatives to optimize the resulting model.
• Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance.

Applications to Open Scientific Problems

• Epidemiological forecasting

  • Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV.
  • Forecasts ranked at or near the top of CDC leaderboards.
  • The techniques can potentially be adapted to other countries and diseases.

• California water-supply forecasting

  • Produced seasonal runoff predictions for snow-fed river basins.
  • Delivered more accurate early forecasts than California’s official Bulletin 120 outlook.
  • Improved predictions could support water management and agriculture.

• Atmospheric carbon dioxide monitoring

  • Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas.
  • Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles.
  • Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2.

• Solar-energy design

  • Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries.
  • Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading.

• Retail forecasting

  • Used economic indicators, Google Trends, historical patterns, and consumer sentiment.
  • Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast.

Computational Discovery

• Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs.
• The system combines ERA with AlphaEvolve to support computational scientific investigation.
• It complements other Gemini for Science experiments:
  • Hypothesis Generation, built with AI Co-Scientist, supports developing scientific hypotheses.
  • Literature Insights supports research and literature analysis.

ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.