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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Any Data is AI-Ready via Semantic Knowledge Graphs
Author(s) Nick Garabedian
On-Site Speaker (Planned) Nick Garabedian
Abstract Scope Unlocking the full potential of AI in materials science requires moving beyond static archiving to dynamic, structured data management. We present a semantic knowledge graph approach that places physical experiments, numerical simulations, AI models, and data analysis on the same conceptual plane. By capturing rich provenance at the source using visual, semantically typed workflows, we aim to mitigate the "data wrangling" bottleneck that often hinders AI initiatives. As a use case, we focus on tribology, where material performance relies on a high-dimensional context. We demonstrate how this knowledge-graph-driven framework enables efficient navigate ion and query across these diverse resources. By utilizing persistent identifiers to facilitate collaboration across different labs, this infrastructure harmonizes complex, multi-modal data for downstream AI applications, including data fusion and quality enhancement. Ultimately, this approach provides a blueprint for integrating scattered research outputs into a cohesive, high-quality environment that supports reproducibility and helps accelerate materials discovery.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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A Multidimensional Continuous Conditional Generative Adversarial Network to Link Composition-Process-Microstructure-Properties in Mg Alloys
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Agentic AI-Driven Design of Refractory Multi-Principal Element Alloys
Agentic AI for Assessing and Optimizing Scientific Reasoning and Creativity in Chemical Vapor Deposition Diamond Synthesis
Agentic Lifestyles in the Era of AI: Research Workflows and Insights from the Vertical Cloud Lab @ BYU
Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
AI-driven theoretical frameworks to determine structure of complex materials and defects from experiments
AI-Enabled High Throughput Discovery of High Entropy Alloys for Extreme Environments
An Automated Probabilistic Machine Learning Framework for Materials Modeling and Active Learning: Benchmarking Uncertainty Quantification Across Analytical, Computational, and Experimental Domains
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Assessing Uncertainty and Reproducibility in ML-Based Grain Growth Predictions: Impact of Training Data
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Development of Machine-Learning Interatomic Potential forSolute–Dislocation Interactions in Fe–C–Cu Steels
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Graph Neural Network Optimization of Industrial Materials, Formulations, and Chemical Processes
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Interpretable Machine Learning and Topological Descriptors for Structural Evolution, Interfaces, and Dopant Thermodynamics: A Halide Perovskites Case Study
Interrogating Materials Informatics Models Along Experimentally Relevant Alloying Pathways
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