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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 COD'HEM: An Integrated Experimental–DFT Materials Informatics Platform for Data-Driven Discovery of High Entropy Materials
Author(s) Suyash Varshney, Sriram Vishnubhotla, Dilpuneet Aidhy
On-Site Speaker (Planned) Suyash Varshney
Abstract Scope The Consolidated Database of High Entropy Materials (COD'HEM) is an open-access platform that brings together experimental literature data and first-principles density functional theory (DFT) data for high entropy materials. The platform began as a curated database of experimentally reported properties extracted from the literature and has now been expanded to include DFT-computed properties within the same framework. This allows users to directly compare experimental and computational results for different alloy compositions through an interactive web interface. The combined dataset also provides a foundation for developing machine learning models for property prediction and accelerated materials screening. Each experimental record is linked to its original publication through its DOI, while the computational data are organized in a consistent format to ensure data quality and traceability. By combining literature data, DFT calculations, and machine learning, COD'HEM provides a practical materials informatics platform for data-driven discovery of high entropy materials.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, High-Entropy Alloys, ICME

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