Materials Informatics
Data-driven materials discovery using databases, simulations, machine learning, and high-throughput experiments.
Core metadata
- ID: materials_informatics
- Era: Modern
- First known date: 1983 (decade)
- Region: Global / multiple regions
- Review status: source_checked
- Maturity: emerging
Prerequisites
- Advanced Materials Science (advanced_materials_science)
- Machine Learning (Early Algorithms) (machine_learning_early_algorithms)
Dependents
- None.
Fields
Field lanes
- Materials Science & Manufacturing: Materials Discovery
Node sources
- Materials Genome Initiative (NIST, 2026, official_agency) • Supports: node, maturity
Prerequisite edge evidence
Edge/source evidence summary:
- Prerequisite edges: 2
- Average edge confidence: 72%
- Prerequisite sources: 2
- expert_inference: 2
| Prerequisite | Type | Confidence | Evidence level | Note | Sources |
|---|---|---|---|---|---|
| Advanced Materials Science (advanced_materials_science) | enabling | 68% | expert_inference | Advanced Materials Science provides a capability that enables this technology without being the only possible path. |
|
| Machine Learning (Early Algorithms) (machine_learning_early_algorithms) | historical_predecessor | 75% | expert_inference | Machine Learning (Early Algorithms) is an earlier historical predecessor or foundation, not a one-to-one engineering dependency. |
|
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