# GSM Presentation/Verification Framework: Refinement with AiiDA Workflow Manager

This document extends the previous refined concept for a GSM-based presentation/verification framework by outlining how the [AiiDA](https://www.aiida.net/) Python workflow engine can be leveraged for robust, persistent, and extensible workflow management. AiiDA is designed for reproducible computational science and provides a powerful infrastructure for chaining, branching, and persisting scientific workflows.

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## Appendix: AiiDA-based Implementation

### 1. Why AiiDA?

- **Provenance Tracking:** Every calculation, input, and output is automatically tracked and stored in a database.
- **Persistence:** All workflow steps and intermediate data are persistent and can be resumed or inspected at any time.
- **Branching and Automation:** Supports complex, conditional workflows with branching, loops, and parameter sweeps.
- **Plugin System:** Easily extendable for new types of calculations or postprocessing.
- **Pythonic API:** Workflows are written as Python classes, making integration with GSMModel and ResponseData straightforward.

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### 2. Mapping GSM Scenarios to AiiDA Workflows

#### 2.1. Calculation Nodes

- **GSMCalculation:** A custom AiiDA Calculation plugin that wraps a GSMModel simulation (e.g., get_F_response or get_G_response).
- **Inputs:** Model definition, parameter set, loading scenario (strain/stress/time arrays).
- **Outputs:** ResponseData (as a serialized object or HDF5/JSON), postprocessed results (e.g., peak stress).

#### 2.2. WorkChain Nodes

- **GSMScenarioWorkChain:** A WorkChain for a single scenario (e.g., monotonic tension, cyclic loading).
  - Prepares input arrays.
  - Runs GSMCalculation.
  - Runs postprocessing steps.
  - Stores results as outputs.

- **GSMWorkflow:** A higher-level WorkChain that chains multiple GSMScenarioWorkChains.
  - Passes outputs from one scenario as inputs to the next.
  - Supports conditional branching (e.g., if peak stress < threshold, skip next scenario).
  - Can implement calibration loops or parameter sweeps.

#### 2.3. Data Nodes

- **GSMModelData:** Stores the symbolic model definition and parameter set.
- **GSMResponseData:** Stores the simulation results and metadata.
- **GSMPostprocessData:** Stores extracted features (e.g., peak stress, critical strain).

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### 3. Example: AiiDA Workflow Skeleton

```python
from aiida.engine import WorkChain, calcfunction
from aiida.orm import Dict, ArrayData

@calcfunction
def run_gsm_simulation(model_dict, scenario_dict):
    # model_dict: contains GSMModel definition and parameters
    # scenario_dict: contains loading arrays and scenario type
    # Run the simulation using GSMModel and return results as ArrayData/Dict
    ...
    return {'response': ArrayData(...), 'features': Dict(...)} 

class GSMScenarioWorkChain(WorkChain):
    @classmethod
    def define(cls, spec):
        super().define(spec)
        spec.input('model', valid_type=Dict)
        spec.input('scenario', valid_type=Dict)
        spec.outline(
            cls.prepare_inputs,
            cls.run_simulation,
            cls.postprocess,
        )
        spec.output('response', valid_type=ArrayData)
        spec.output('features', valid_type=Dict)

    def prepare_inputs(self):
        # Prepare scenario input arrays, possibly using previous results
        pass

    def run_simulation(self):
        result = run_gsm_simulation(self.inputs.model, self.inputs.scenario)
        self.ctx.response = result['response']
        self.ctx.features = result['features']

    def postprocess(self):
        self.out('response', self.ctx.response)
        self.out('features', self.ctx.features)

class GSMWorkflow(WorkChain):
    @classmethod
    def define(cls, spec):
        super().define(spec)
        spec.input('model', valid_type=Dict)
        spec.input('scenarios', valid_type=Dict)
        spec.outline(
            cls.run_scenarios,
            cls.finalize,
        )
        spec.output('all_features', valid_type=Dict)

    def run_scenarios(self):
        # Loop or branch over scenarios, submitting GSMScenarioWorkChains
        pass

    def finalize(self):
        # Aggregate results, possibly update parameters, etc.
        pass
```

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### 4. Benefits and Considerations

- **Reproducibility:** All data and workflow steps are stored with full provenance.
- **Scalability:** Can run large parameter sweeps or calibration loops in parallel.
- **Inspection:** Intermediate and final results can be queried and visualized at any time.
- **Integration:** Can be combined with other AiiDA plugins for experimental data, optimization, or machine learning.

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### 5. Next Steps

1. Implement minimal AiiDA Calculation and WorkChain plugins for GSMModel.
2. Define serialization/deserialization for GSMModel and ResponseData (e.g., using JSON or HDF5).
3. Create scenario templates as AiiDA WorkChains.
4. Document how to launch, monitor, and analyze workflows using AiiDA's CLI and Python API.
5. Optionally, integrate with Jupyter notebooks for interactive workflow construction and result visualization.

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### 6. References

- [AiiDA Documentation](https://aiida.readthedocs.io/)
- [AiiDA Tutorials](https://aiida-tutorials.readthedocs.io/)
- [AiiDA Plugin Registry](https://aiidateam.github.io/aiida-registry/)

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By leveraging AiiDA, the GSM presentation/verification framework gains robust workflow management, provenance, and extensibility, making it suitable for both research and production-scale computational materials science.
