Session Sheet - Joint Sessions of AIM, ICME, & 3DMS: Fair Data II

Joint Sessions of AIM, ICME, & 3DMS: Fair Data II
Program Organizers: TMS Administration

Monday 1:40 PM
June 16, 2025
Room: Platinum Ballroom 7&8
Location: Anaheim Marriott

Session Chair: William Chen, Nswc Carderock

1:40 PM
Enhancing AI Readiness Through Data Stewardship, Modular Ontologies, and FAIR Data Workflows: Balashanmuga Priyan Rajamohan¹; Kai Zheng¹; Van Tran¹; Nathaniel Hahn¹; Hayden Caldwell¹; Alexander Bradley¹; Quynh Tran¹; Laura Bruckman¹; Yinghui Wu¹; Erika Barcelos¹; Roger French¹; ¹Sdle Research Center
    The complexity of materials science data complicates collaboration and scientific progress due to inconsistent terminologies and formats. Our solution is an ecosystem for modular ontology development grounded in FAIR (Findable, Accessible, Interoperable, Reusable) principles. At its center is MDS Onto, a unified ontology harmonizing material science domains to reduce redundancy and enforce consistency. Our OntoPortal is an internal ontology repository that helps users select standardized terms when creating new ontologies. Additionally, the FAIRMaterials package enables scientists to develop domain-specific ontologies linked to the Basic Formal Ontology, supporting advanced reasoning. The package uses MDS Onto to generate data ingestion sheets that convert experimental data into FAIR-aligned Linked Data and offers an interactive web app, "Datapedia," for local datasets. This ecosystem fosters collaboration and innovation by enhancing data interoperability and establishing standardized ontology practices in materials science.

2:00 PM  Cancelled
Practical Data Management in Computational Materials for Qualification and Certification: Andrew Kitahara¹; George Weber²; Edward Glaessgen²; ¹Analytical Mechanics Associates; ²NASA Langley Research Center
    Qualification and certification (Q&C) processes impose specific requirements for aviation flight hardware to prove predictability of material and part performance, reliability, and safety. Additive manufacturing (AM) methodologies such as powder bed fusion (PBF) create unique opportunities for lightweight, integrated flight hardware systems, but the Q&C requirements often negate the value of AM insertion, which is a core motivation discussed in the computational materials-informed for qualification and certification (CM4QC) roadmap. To fully integrate computational materials methods with physical AM material testing, a thorough data management platform should be developed. This presentation will present ongoing efforts to develop an infrastructure to support AM research of coupled modeling and physical testing. The goal of the data platform is to prototype and demonstrate CM4QC processes at the laboratory scale and later evolve to serve production Q&C applications. Specific points of discussion will include the schema developments, requirements-driven experimental plans, and implementation procedures.

2:20 PM
The Materials Science and Engineering Knowledge Graph: Establishing a Centralized Metadata Index for Enhanced Data Integration : Ebrahim Norouzi¹; Hossein Beygi Nasrabadi¹; Jörg Waitelonis¹; Harald Sack¹; ¹FIZ-Karlsruhe
     The Materials Science and Engineering Knowledge Graph (MSE-KG) [1] serves as a central repository that semantically connects metadata from diverse sources, creating a unified, searchable data space in Materials Science and Engineering. Its semantic backbone, the MatWerk Ontology (MWO) [2], extends concepts from the NFDI-core ontology [3], enabling efficient data discovery within the MSE domain. By aligning local data infrastructure with the MSE-KG, organizations can continuously feed metadata into this centralized framework, fostering cross-domain data queries and exchanges while allowing data providers to retain control. This structure enhances the visibility and accessibility of MSE resources, advancing innovation in the field. References [1] https://go.fzj.de/mse-kg [2] http://purls.helmholtz-metadaten.de/mwo/[3] https://github.com/ISE-FIZKarlsruhe/nfdicore

2:40 PM
X-Ray Diffraction Analysis Using TensorFlow and FAIR Data Pipelines: Finley Holt¹; Daniel Savage²; Mohommad Mehdi¹; Weiqi Yue¹; Pawan Tripathi¹; Matthew Willard¹; Frank Ernst¹; Roger French¹; Quynh Tran¹; ¹Case Western Reserve University; ²Los Alamos National Laboratory
    X-ray diffraction generates vast, complex datasets of material behavior that demand scalable and flexible scientific analyses. The efficient management and manipulation of image and histogram data has largely been addressed in the development of the TensorFlow package for ML and AI. In this talk we will explore using our newly developed FAIRshake package, an end-to-end, modular framework that interfaces with FAIRified diffraction data using the TensorFlow Dataset API, to perform data manipulation and analysis. TensorFlow allows data transformation (e.g dark corrections, azimuthal integration, analysis) to be performed using standard TensorFlow dataset tools. The autotuning capability of TensorFlow enables excellent performance, from desktops to HPC environments, through dynamic dataset streaming and parallelization. TensorFlow datasets are shown through examples to be especially attractive for scientific analysis that can natively utilize tensor representations of data; bringing into focus the question: “How should scientific codes be interacting with FAIR data?”