---
name: scientific-prediction-f040cc83936e
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
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

## 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


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/scientific-prediction/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/scientific-prediction/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Finance & Accounting, Marketing, Product Management
Roles: Data Scientist, Financial Analyst, Growth Marketer, Market Research Analyst, Product Manager
Specialties: Specialty not specified
Tools: Fred, Materials Project
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: c92f2ca46f5a3bc0722d23857fa30882c0dfc91416cf1b2573ca02ca0b3576d0