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

1. Problem formulation — Define target variable, features, and prediction horizon
2. Data preparation — Feature engineering, normalization, train/test split
3. Model selection — Choose appropriate model class (regression, time series, ML, physics-informed)
4. Training & validation — Fit model, cross-validate, tune hyperparameters
5. Prediction & uncertainty — Generate predictions with confidence intervals
6. Evaluation — Report metrics (RMSE, MAE, R², MAPE) and compare to baselines