What’s next for digital twins?
While some of Berkeley Lab’s digital twins are already set to guide experiments, others are still in research and development. Researchers across disciplines are adapting methods from existing physics and energy models to unlock more powerful experiments.
Developing biological digital twins to accelerate biofuel production
The Advanced Biofuels and Bioproducts Process Development Unit (ABPDU) is developing a biological digital twin to model the scaled production of lipids for jet fuel from engineered microbes. Modeling living microbes within a bioreactor is more complex than modeling purely physical or chemical systems, since microorganisms constantly grow, divide, produce materials, and die, all within communities that interact in ways that are more difficult to predict.
Funded by DOE’s Advanced Fuels and Feedstocks Office, the researchers are developing novel methods to obtain imaging and phenotypic data to integrate with large, genome-wide omics datasets. Separately, the team will combine mechanistic models, which evaluate bioreactor design and performance, with machine-learning models that can analyze datasets to develop their digital twin. This hybrid approach uses high-performance computing facility Perlmutter at NERSC for large-scale simulations and will yield a richly structured ensemble model — much more predictive of technology performance than either type of model alone — allowing it to capture the complicated dynamics of microbial cultures. This research is a natural next step in ABPDU’s efforts to advance the science of scale-up and energy innovation.
“The global economy is changing as countries and markets rush to put biology to work in new ways. The U.S. has both the expertise and the opportunity to use AI and machine learning to gain a technological edge,” said James Gardner, Program Manager at the ABPDU. “This could help secure new intellectual property and grow domestic biomanufacturing capacity.”
A digital twin powering an AI agent to optimize particle accelerators
As the Advanced Light Source (ALS) transitions to the ALS-U era, the legacy injector complex must meet significantly more demanding beam stability and quality requirements. ATAP physicists at ALS are developing a digital twin to enhance performance and automate optimization of the injector, the initial section of the ALS accelerator complex. Funded by BES and aligned with the Genesis Mission, this AI/ML research effort aims to create a “virtual diagnostic”. This specialized type of digital twin will initially enable accelerator operators and physicists at the ALS to assess beam quality at the end of the injector without perturbing the beam or interrupting user operations, unlike the current method that relies on destructive measurements. In a subsequent step, building on top of a recently demonstrated AI-driven automated bootstrapping process for initial startup of the cold injector, this digital twin can then be employed by an agentic AI assistant capable of providing real-time insights and optimizing injector performance in an automated manner across various operational scenarios. This approach ensures that the performance of an aging and drifting injector remains optimized and stable during user operations without operator intervention or relying on interruptions to user beam conditions. Additionally, it will support the development of a comprehensive digital representation of the entire legacy ALS injector complex, ensuring its continued capability to provide for the brand-new and highly demanding ALS-U storage ring for many years to come.
Making digital twins adaptable across accelerator experiments
Berkeley Lab is also leading a multi-lab team to deploy AI/ML tools on DOE accelerator facilities, as one of the first demonstrations of the Genesis Mission’s American Science Cloud (AmSC) — a new DOE system to connect supercomputers, advanced networks, and user facilities across the national lab complex. Part of this effort will consist of generalizing the framework that was built for the BELLA Center digital twin prototype and deploying it using the infrastructure and APIs created under AmSC. By standardizing API interfaces and data-exchange, the research will allow tools to work for any type of particle accelerator across facilities without needing to be reconfigured, enabling digital twins and other models to be applied to different experiments. This work will be reinforced by another multi-lab team led by Berkeley Lab within the Genesis Transformational AI Models Consortium (ModCon), the Multi-Office Particle Accelerator Team (MOAT), which will further establish the technical foundation and collaborative framework for the ongoing AI-powered development of the DOE accelerator complex, sustaining U.S. leadership in accelerator science. In addition, this work seeks to leverage the connections with Berkeley Lab’s participation in the Genesis AI-Ready DOE Fusion Energy Sciences project on “Digital Twins and Data Integration for Accelerated Design and Operation of Inertial Fusion Energy Power Plant Systems,” led by Lawrence Livermore National Laboratory. For the American public, that means faster progress on national priorities.
“By making digital twins more adaptable and compatible across facilities, the Genesis teams will enable accelerated discoveries in various applications of particle accelerators, including energy, advanced materials, fundamental physics, and advanced medical technologies,” said Jean-Luc Vay, head of the Advanced Modeling Program in the Accelerator Technology & Applied Physics (ATAP) Division.
The ESnet, ALS, and NERSC are Office of Science User Facilities.
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