II.
Workflow overview
Reference · liveworkflow:material-characterization-pipeline
Material Characterization Pipeline overview
Processes material characterization data through automated pipelines -- ingesting XRD, SEM/TEM, DSC/TGA, and tensile test outputs, applying peak-fitting and phase identification algorithms, cross-referencing against ICDD/PDF databases, computing derived properties (Young's modulus, glass transition), and generating standardized material property cards for the materials database. Excludes sample preparation.
Attributes
displayName
Material Characterization Pipeline
workflowKind
operational
triggerType
event-driven
typicalCadence
per-sample-batch
complexity
single-team
description
Processes material characterization data through automated pipelines
-- ingesting XRD, SEM/TEM, DSC/TGA, and tensile test outputs,
applying peak-fitting and phase identification algorithms,
cross-referencing against ICDD/PDF databases, computing derived
properties (Young's modulus, glass transition), and generating
standardized material property cards for the materials database.
Excludes sample preparation.
Outgoing edges
applies_to_domain2
- domain:materials-science·DomainMaterials Science
- domain:scientific-computing·DomainScientific Computing
involves_role3
- role:data-scientist·RoleData Scientist
- role:ml-engineer·RoleMachine Learning Engineer
- role:staff-engineer·RoleStaff Engineer
performed_by_org_unit2
- org-unit:research-engineering·OrgUnitResearch Engineering
- org-unit:data-team·OrgUnitData Team
requires_skill_area2
- skill-area:python-data-pipelines·SkillAreaPython Data Pipelines
- skill-area:ml-fine-tuning·SkillAreaML Fine-Tuning
triggers_responsibility1
- responsibility:data-quality-monitoring·ResponsibilityData quality monitoring
Incoming edges
None.