By name: scientific-prediction description: Predict material properties, economic indicators, and scientific outcomes using computational models
Scientific Prediction & Simulation
Purpose
Predict scientific outcomes, material properties, and time series using computational models and simulation.
Key Datasets
- Materials Project (materials-toolkits/materials-project): 133K+ materials with DFT-computed properties (band gap, formation energy, elastic moduli, etc.)
- FRED (fred.stlouisfed.org): Federal Reserve Economic Data — macroeconomic time series (GDP, CPI, unemployment, interest rates)
Protocol
- Problem formulation — Define target variable, features, and prediction horizon
- Data preparation — Feature engineering, normalization, train/test split
- Model selection — Choose appropriate model class (regression, time series, ML, physics-informed)
- Training & validation — Fit model, cross-validate, tune hyperparameters
- Prediction & uncertainty — Generate predictions with confidence intervals
- Evaluation — Report metrics (RMSE, MAE, R², MAPE) and compare to baselines
Prediction Domains
- Materials properties: Band gap, formation energy, thermal conductivity, hardness
- Economic forecasting: GDP growth, inflation, employment, market indices
- Molecular properties: Solubility, toxicity, binding affinity, ADMET
- Climate modeling: Temperature trends, precipitation patterns, extreme events
Rules
- Always report prediction uncertainty/confidence intervals
- Compare against meaningful baselines (not just random)
- Validate on held-out data (never evaluate on training data)
- For materials predictions, verify physical plausibility (positive energies, reasonable ranges)
- For economic predictions, note structural breaks and regime changes