One compelling example is the 3D printing of metal alloys, such as titanium and iron, using laser powder bed fusion (LPBF). This complex and data-rich process involves multiple interdependent steps, including powder preparation, parameter tuning, in-situ monitoring, post-processing, and multi-scale characterization. Capturing and standardizing these steps into a machine-readable format, such as Common Workflow Descriptions (CWDs), enables reproducibility, transparency, and interoperability across different research facilities. This formalized workflow ensures that experimental procedures, metadata, and decision logic are clearly documented, which is especially critical in collaborative environments like Materials Acceleration Platforms (MAPs). By adopting this structured approach, researchers can reduce setup time, minimize human error, and seamlessly integrate the workflows with machine learning pipelines for accelerated discovery.

In parallel, rapid prototyping of glassy composites demonstrates how optimizing glass formulations can tailor optical and mechanical properties. Automated high-throughput characterization tools are tightly coupled with ML algorithms that guide the iterative design cycles, optimizing print parameters and predicting material performance. Together, these examples illustrate how advanced additive manufacturing techniques combined with automated workflows and data-driven optimization can significantly shorten the materials development loop, enabling faster innovation for aerospace, energy storage, and biomedical applications.

• Capture detailed process metadata from high-throughput experiments, including print parameters, thermal profiles, and post-processing steps.
• Develop standardized workflows and metadata schemas (e.g., Common Workflow Descriptions) to support reproducibility, transparency, and interoperability.
• Implement FAIR, automated, and scalable research data infrastructures for Materials Acceleration Platforms (MAPs).
• Enable iterative, machine-learning-guided workflows to optimize materials faster and reduce manual intervention.
• Demonstrate workflow integration and metadata handling in real-world use cases such as metal additive manufacturing and glass formulation.