MD
janosh/matbench-discovery
An evaluation framework for machine learning models simulating high-throughput materials discovery.
217 55 +0/wk
GitHub ✦ New Signal
bayesian-optimization convex-hull high-throughput-search interatomic-potential machine-learning materials-discovery
Trend
0
Star & Fork Trend (2 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
janosh/matbench-discovery has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | matbench-discovery | vigenair | vscode-dvc | PerforatedAI |
|---|---|---|---|---|
| Stars | 217 | 217 | 218 | 216 |
| Forks | 55 | 66 | 29 | 76 |
| Weekly Growth | +0 | -1 | +0 | +0 |
| Language | Python | TypeScript | TypeScript | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | Apache-2.0 | Apache-2.0 | Apache-2.0 |
Capability Radar vs vigenair
matbench-discovery
vigenair
Maintenance Activity 100
Last code push 0 days ago.
Community Engagement 100
Fork-to-star ratio: 25.3%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.