Development of a Modular Digital Twin to Enable an AI-Powered Laboratory

Challenge:

Process chemistry teams were eager to leverage machine learning to accelerate experimentation, but they faced significant barriers to adoption. Existing As the client began leveraging large language models (LLMs) to generate experimental protocols, new challenges emerged around safety, validation, and system orchestration. There was no infrastructure in place to simulate or validate LLM-generated steps before executing them on physical lab hardware. Existing workcell vendor software operated in silos, limiting protocol execution to individual modules without coordination across systems. Additionally, the lab lacked a unified interface to visualize and manage end-to-end processes, making it difficult to ensure consistency and reliability. The client needed a real-time simulation environment that could account for dynamic scheduling, device states, and future integration with predictive analytics tools – all within a standards-compliant architecture.

Solution:

To meet these needs, the team developed a modular digital twin platform using Django and PostgreSQL as the backend. This system modeled the client’s lab automation environment and exposed simulation and validation endpoints via Django REST Framework and Django Ninja, enabling automated testing directly from LLM pipelines. Ignition Perspective dashboards were created to allow users to monitor the physical system or switch to a digital twin environment for protocol testing. The platform supported parallel testing of multiple digital twin instances, enabling rapid validation and error detection. Standardized user interfaces were implemented across work cells to ensure consistency and scalability. Throughout the project, the team collaborated closely with client engineers to co-develop an architecture that mirrored real-world lab workflows and constraints.

Results:

The digital twin platform now enables the client to validate LLM-generated protocols in a simulated environment before live deployment. This significantly reduces lab downtime and prevents costly equipment errors by flagging structural, logical, and operational issues in advance. Operators can seamlessly toggle between physical and digital twin environments for training, testing, and development. The API-first architecture supports rapid iteration and integration with AI/ML models, schedulers, and orchestration layers. With this foundation in place, the client is well-positioned to scale toward a fully AI-integrated lab platform featuring closed-loop automation and real-time simulation capabilities.

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