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The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

This system enables the modular design and optimization of complex plant traits by organizing genes and regulatory mechanisms into interpretable clades.

This invention provides a platform that uses AI agents and biological networks to uncover and interpret disease-relevant biological mechanisms.

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

Digital twins (DTs) have emerged as essential tools for monitoring, predicting, and optimizing physical systems by using real-time data.

Simulation cloning is a technique in which dynamically cloned simulations’ state spaces differ from their parent simulation due to intervening events.

Electrochemistry synthesis and characterization testing  typically occurs manually at a research facility.