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Bridging eResearch Infrastructure and Experimental Materials Science Process in the Quantum Data Hub

Amarnath Gupta, Shweta Purawat, Subhasis Dasgupta, Pratyush Karmakar, Elaine Chi, Ilkay Altintas

TL;DR

The paper tackles democratizing access to AI-enabled experimental materials science by introducing the Quantum Data Hub (QDH), a community-oriented infrastructure that interoperates with the National Data Platform. It advances a knowledge-network design built on FAIR principles augmented by UNIT (Usability, Navigability, Interpretability, Timeliness) to support diverse users and rapid data access, while extending material-synthesis semantics with GEMD++ to capture end-to-end processes across heterogeneous storage. The contributions include a graph-based GEMD++ model with Runs/Specs and a socio-technical extension for multi-lab collaborations, a polystore storage architecture for integrated data management, and an analysis interface via JupyterHub for scalable computation. The framework enables reproducible, cross-lab collaboration by providing standardized procedure editing, bulk data ingestion, controlled data sharing, and cross-institution services, potentially accelerating discovery in quantum materials synthesis. The approach formalizes access rights as $Z = Z_g + Z_d$, combining group-based and discretionary permissions to balance openness with security across a federated research ecosystem.

Abstract

Experimental materials science is experiencing significant growth due to automated experimentation and AI techniques. Integrated autonomous platforms are emerging, combining generative models, robotics, simulations, and automated systems for material synthesis. However, two major challenges remain: democratizing access to these technologies and creating accessible infrastructure for under-resourced scientists. This paper introduces the Quantum Data Hub (QDH), a community-accessible research infrastructure aimed at researchers working with quantum materials. QDH integrates with the National Data Platform, adhering to FAIR principles while proposing additional UNIT principles for usability, navigability, interpretability, and timeliness. The QDH facilitates collaboration and extensibility, allowing seamless integration of new researchers, instruments, and data into the system.

Bridging eResearch Infrastructure and Experimental Materials Science Process in the Quantum Data Hub

TL;DR

The paper tackles democratizing access to AI-enabled experimental materials science by introducing the Quantum Data Hub (QDH), a community-oriented infrastructure that interoperates with the National Data Platform. It advances a knowledge-network design built on FAIR principles augmented by UNIT (Usability, Navigability, Interpretability, Timeliness) to support diverse users and rapid data access, while extending material-synthesis semantics with GEMD++ to capture end-to-end processes across heterogeneous storage. The contributions include a graph-based GEMD++ model with Runs/Specs and a socio-technical extension for multi-lab collaborations, a polystore storage architecture for integrated data management, and an analysis interface via JupyterHub for scalable computation. The framework enables reproducible, cross-lab collaboration by providing standardized procedure editing, bulk data ingestion, controlled data sharing, and cross-institution services, potentially accelerating discovery in quantum materials synthesis. The approach formalizes access rights as , combining group-based and discretionary permissions to balance openness with security across a federated research ecosystem.

Abstract

Experimental materials science is experiencing significant growth due to automated experimentation and AI techniques. Integrated autonomous platforms are emerging, combining generative models, robotics, simulations, and automated systems for material synthesis. However, two major challenges remain: democratizing access to these technologies and creating accessible infrastructure for under-resourced scientists. This paper introduces the Quantum Data Hub (QDH), a community-accessible research infrastructure aimed at researchers working with quantum materials. QDH integrates with the National Data Platform, adhering to FAIR principles while proposing additional UNIT principles for usability, navigability, interpretability, and timeliness. The QDH facilitates collaboration and extensibility, allowing seamless integration of new researchers, instruments, and data into the system.
Paper Structure (16 sections, 5 figures, 1 table)

This paper contains 16 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Related Platforms that Enable Material Science
  4. Related Data Systems that Facilitate Collaboration in Material Science
  5. Design of the Quantum Data Hub
  6. FAIR and UNIT: Design Pillars of QDH
  7. GEMD++: Semantic Infrastructure of QDH
  8. GEMD Component
  9. GEMD Extension
  10. Polystore-Based Storage and Implementation
  11. Scalable Analysis Interface using Jupyterhub
  12. Facilitating Science and Collaborations
  13. Facilitating Science
  14. Adding New Collaborations
  15. Facilitate sharing of data and materials synthesis procedure through Access Control
  16. ...and 1 more sections

Figures (5)

  • Figure 1: A simplified version of relational schema maintained at QDH
  • Figure 2: The material history of the sample specified by the query. The result has 4 samples (yellow), 15 process_runs (blue), 11 material and ingredient nodes (light purple and orange) nodes.
  • Figure 3: A screenshot showing a collection of discovered properties of $Sr_2IrO_4$ from multiple scientific papers.
  • Figure 4: The left shows Jupyter Notebook server configuration interface and right side shows API calls to the get the data from QDH cloud storage to JN
  • Figure 6: QDH Authentication Protocol sequence diagram illustrating the Security Protocols.