A collaborative digital twin built on FAIR data and compute infrastructure

2025-06-24

AI Summary

This study addresses the challenges of fragmented data sharing and delayed model updates among geographically distributed teams in self-driving laboratories (SDLs). Methodologically, we propose a collaborative digital twin framework grounded in the FAIR principles: (1) a distributed infrastructure integrating nanoHUB Sim2L for automated data extraction and indexing; (2) ResultsDB as a FAIR-compliant data warehouse; (3) a web interface enabling multi-source data submission; and (4) nanoHUB workflows coupling active learning with online machine learning to enable dynamic model updating and closed-loop experimental optimization. Innovatively, we introduce the “frugal twin” paradigm—demonstrated via low-cost food-grade dye color formulation—to broaden accessibility and scalability. Our contribution is a scalable, FAIR-enabled collaborative optimization framework that significantly improves cross-regional scientific discovery efficiency. Empirical validation on color formulation optimization confirms both system feasibility and generalizability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by findable, accessible, interoperable, and reusable (FAIR) data infrastructure, SDLs with overlapping interests can collaborate more effectively. This work presents a distributed SDL implementation built on nanoHUB services for online simulation and FAIR data management. In this framework, geographically dispersed collaborators conducting independent optimization tasks contribute raw experimental data to a shared central database. These researchers can then benefit from analysis tools and machine learning models that automatically update as additional data become available. New data points are submitted through a simple web interface and automatically processed using a nanoHUB Sim2L, which extracts derived quantities and indexes all inputs and outputs in a FAIR data repository called ResultsDB. A separate nanoHUB workflow enables sequential optimization using active learning, where researchers define the optimization objective, and machine learning models are trained on-the-fly with all existing data, guiding the selection of future experiments. Inspired by the concept of ``frugal twin", the optimization task seeks to find the optimal recipe to combine food dyes to achieve the desired target color. With easily accessible and inexpensive materials, researchers and students can set up their own experiments, share data with collaborators, and explore the combination of FAIR data, predictive ML models, and sequential optimization. The tools introduced are generally applicable and can easily be extended to other optimization problems.

Problem

• Accelerate discovery via FAIR data and self-driving labs
• Enable collaborative optimization using shared FAIR databases
• Develop frugal digital twin for color recipe optimization

Innovation

• Collaborative digital twin using FAIR data
• Machine learning with automated experimentation
• Distributed SDL implementation on nanoHUB