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Materials Discovery Workflow

This page illustrates how the nomad-dtu-nanolab-plugin schemas connect in a complete materials discovery workflow, from lab inventory through synthesis, characterization, and analysis. Understanding this flow helps you see how individual schemas work together to capture the entire research process.

Schema Package Organization

The plugin contains 17 schema packages organized by function. This organization mirrors the natural workflow progression in the lab:

graph TB
    subgraph "Lab Inventory & Items"
        A[samples<br/>DTUCombinatorialLibrary<br/>DTUCombinatorialSample]
        B[substrates<br/>DTUSubstrate<br/>DTUSubstrateBatch]
        C[targets<br/>DTUTarget]
        D[gas<br/>DTUGasSupply]
        E[instruments<br/>DTUInstrument]
    end

    subgraph "Synthesis & Processing"
        F[sputtering<br/>DTUSputtering]
        G[rtp<br/>DtuRTP]
        H[thermal<br/>Thermal Evaporation]
        I[cleaving<br/>DTULibraryCleaving]
    end

    subgraph "Characterization"
        J[basesections<br/>Base Measurement]
        K[xrd<br/>DTUXRDMeasurement]
        L[xps<br/>DTUXpsMeasurement]
        M[edx<br/>EDXMeasurement]
        N[pl<br/>DTUPLMeasurement]
        O[ellipsometry<br/>DTUEllipsometryMeasurement]
        P[raman<br/>RamanMeasurement]
        Q[rt<br/>RTMeasurement]
    end

    subgraph "Data Analysis"
        R[analysis<br/>DtuJupyterAnalysis]
    end

    style A fill:#e1f5ff
    style B fill:#e1f5ff
    style C fill:#e1f5ff
    style D fill:#e1f5ff
    style E fill:#e1f5ff
    style F fill:#fff4e1
    style G fill:#fff4e1
    style H fill:#fff4e1
    style I fill:#fff4e1
    style J fill:#ffe1f5
    style K fill:#ffe1f5
    style L fill:#ffe1f5
    style M fill:#ffe1f5
    style N fill:#ffe1f5
    style O fill:#ffe1f5
    style P fill:#ffe1f5
    style Q fill:#ffe1f5
    style R fill:#e1ffe1

Color coding: - 🔵 Blue = Lab Inventory & Items (Entities) - 🟡 Yellow = Synthesis & Processing (Activities) - 🔴 Pink = Characterization (Activities) - 🟢 Green = Data Analysis (Activities)

End-to-End Workflow Example

Here's how these schemas connect in a typical DTU Nanolab workflow, from inventory to analysis:

graph LR
    subgraph "1. Lab Inventory"
        A[DTUSubstrateBatch<br/>Batch of substrates]
        B[DTUTarget<br/>Sputter targets]
        C[DTUGasSupply<br/>Process gases]
        D[DTUInstrument<br/>Sputter tool]
    end

    subgraph "2. Synthesis"
        E[DTUSputtering<br/>Deposition process]
        F[DTUCombinatorialLibrary<br/>Material library with<br/>composition gradient]
    end

    subgraph "3. Sample Position Mapping"
        S[DTUCombinatorialSample<br/>Sample positions at<br/>specific coordinates]
    end

    subgraph "4. Optional Physical Cleaving"
        G[DTULibraryCleaving<br/>Split into pieces]
        H[Child Libraries<br/>Physical pieces containing<br/>multiple sample positions]
    end

    subgraph "5. Characterization"
        I[DTUXRDMeasurement<br/>Crystal structure]
        J[DTUXpsMeasurement<br/>Surface composition]
        K[DTUPLMeasurement<br/>Optical properties]
    end

    subgraph "6. Analysis"
        L[DtuJupyterAnalysis<br/>Data processing]
    end

    A -->|uses substrate from| E
    B -->|uses target| E
    C -->|uses gas| E
    D -->|performed on| E
    E -->|creates| F
    F -->|defines positions on| S
    F -.->|optional: split| G
    G -.->|creates pieces| H
    S -->|references coord on| F
    S -.->|or on cleaved| H
    S -->|measured at position| I
    S -->|measured at position| J
    S -->|measured at position| K
    I -->|data fed to| L
    J -->|data fed to| L
    K -->|data fed to| L

    style A fill:#e1f5ff
    style B fill:#e1f5ff
    style C fill:#e1f5ff
    style D fill:#e1f5ff
    style E fill:#fff4e1
    style F fill:#e1f5ff
    style S fill:#e1f5ff
    style G fill:#fff4e1
    style H fill:#e1f5ff
    style I fill:#ffe1f5
    style J fill:#ffe1f5
    style K fill:#ffe1f5
    style L fill:#e1ffe1

Key Workflow Concepts

  • Sample positions (DTUCombinatorialSample) are defined by coordinates on libraries, not by physical cleaving
  • Cleaving (optional) creates physical pieces (child libraries) for parallel processing
  • Measurements reference libraries and track their coordinates, whether on intact libraries or cleaved pieces
  • A single cleaved piece can contain multiple sample positions at different compositions

Workflow Stages Explained

1. Lab Inventory Setup

Before starting experiments, you document your lab's resources:

  • Substrate batches: Catalog wafers/substrates with batch numbers and properties
  • Targets: Document sputter targets with composition and usage tracking
  • Gas supplies: Register gas cylinders with purity and cylinder numbers
  • Instruments: Define lab equipment with capabilities and configurations

These are all entities—persistent physical items with lab IDs.

2. Synthesis Process

You perform a synthesis activity that consumes inventory items and creates libraries:

The process extends NOMAD's Process activity class, automatically linking inputs and outputs.

3. Optional Physical Cleaving

If needed for parallel processing, you can physically divide the library:

  • Library cleaving process (DTULibraryCleaving) splits the substrate
  • Input: Parent library
  • Outputs: Multiple child libraries (physical pieces)
  • Sample positions remain unchanged: They still reference their original coordinates
  • Each cleaved piece typically contains multiple sample positions

Learn more about this distinction in Combinatorial Libraries Concept.

4. Characterization Measurements

You perform measurement activities on several library coordinates:

Each measurement:

  • References the specific library and tracks coordinates
  • Links to the instrument used
  • Stores measurement parameters and results
  • Extends the common BaseMeasurement infrastructure

See Characterization Techniques for what each technique provides.

5. Data Analysis

Finally, you process and interpret the data:

  • Jupyter Analysis (DtuJupyterAnalysis) for computational workflows
  • Inputs: References to libraries and their provenance
  • Outputs: Processed results, figures, derived properties
  • Notebook integration: Links to Jupyter notebooks with analysis code

The analysis activity completes the provenance chain: inventory → synthesis → samples → measurements → analysis.

This workflow structure provides:

  1. Reproducibility: Exact conditions documented for every sample
  2. Traceability: Query any sample's complete history
  3. Efficiency: Reuse inventory items across many experiments
  4. Analysis: Correlate synthesis parameters with measured properties
  5. Publications: Auto-generate methods sections from metadata
  6. Collaboration: Share complete provenance with collaborators

Learn More