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ML-extendable framework for multiphysics-multiscale simulation workflow and data management using Kadi4Mat.

Somnath Bharech¹, Yangyiwei Yang², Michael Selzer³

¹Division Mechanics of Functional Materials, Institute of Materials Science, Technical University Darmstadt, Otto-Berndt-Strasse 3, Darmstadt, 64287, Germany.

Scientific Data

|June 9, 2025

Summary

This summary is machine-generated.

This study introduces a research workflow and data management (RWDM) framework to organize material science data. The framework enhances data-driven design and analysis for simulations, particularly in additive manufacturing.

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Area of Science:

Materials Science
Computational Science
Data Management

Background:

Material modeling and simulation are crucial for understanding material behavior across scales.
Effective data management and metadata are essential for data-driven material design.
Current methods often struggle with managing complex simulation workflows and large datasets.

Purpose of the Study:

To propose a Research Workflow and Data Management (RWDM) framework.
To manage complex workflows and research data following FAIR principles.
To showcase the framework using multiphysics-multiscale simulations for additive manufacturing.

Main Methods:

Developed an RWDM framework integrated with Kadi4Mat, an open-source research data infrastructure.
Curated simulation input/output data, setups, and scripts in standardized Kadi4Mat records.
Interlinked records to form an ontology-based knowledge graph and implemented an automation scheme.

Main Results:

Successfully managed complex multiphysics-multiscale simulation data for additive manufacturing.
Created standardized, interlinked records for simulation workflows and associated data.
Demonstrated an automated scheme for high-throughput simulations and post-processing.

Conclusions:

The proposed RWDM framework effectively manages complex simulation workflows and research data.
The framework supports FAIR data principles and facilitates data-driven analyses.
The integration with Kadi4Mat and knowledge graph creation enhances research reproducibility and discoverability.