Multi-Area Topics

We are deepening our understanding of the universe by advancing theory, simulation, and data science and developing state-of-the-art detectors and instrumentation. We explore the fundamental laws of physics by building advanced accelerators and experiments.

FY2026 priorities are reflected below under the general topics listed in Research Theme 5.

We are discovering knowledge about energy and the environment by understanding and directing chemical processes and material phenomena on scales from electrons to molecules to extended systems and by characterizing and controlling biological systems. We are harnessing this knowledge in systems engineering and manufacturing by developing a predictive understanding of complex environmental systems (biotic and/or abiotic).

Priorities in:

• Impacts of disturbances and extremes on the environment – developing new approaches to detection, quantification and mitigation.
• Building biotic/abiotic systems to address energy challenges – combining experimental and computation methods to develop a fundamental and predictive understanding of hybrid systems.

We are driving the future of computing and data science by innovating new mathematical, statistical, and computational methods and realizing the benefits of these novel approaches, such as AI/ML in scientific disciplines. We are innovating methods to optimize and deliver application-ready data for the scientific community and creating models to explore the limits of emergent behavior of physical, chemical, and biological systems. We are co-designing novel computing and network architectures and accelerating the next quantum revolution.

Priorities in:

• AI/ML driven curation, integration and query of complex data – using LLMs and other AI/ML methods to simplify and automate the analysis of diverse complex data for scientific discovery.
• Research directions targeting all layers of the quantum computing and networking stacks. These include materials, qubits, HPC tools and interactions, quantum processors, control hardware, firmware, and software, compilation and optimization tools, and algorithms and applications. Proposals with technically diverse teams with the aim to bridge these layers, and improve performance through a co-design approach are especially encouraged.

We are bringing a science-to-systems approach to meeting the nation’s energy demands by diversifying energy supplies while ensuring the reliability, resilience, and affordability of our energy systems through energy storage and energy efficiency solutions. Rapidly increasing energy demands call for novel approaches that can be commercially deployed quickly and at scale. Berkeley Lab is working with urgency to find creative energy solutions by assembling integrated research teams.

Priorities in:

• Energy Efficient Computing and AI – Accelerate research to reduce the energy requirements of computing systems, from energy efficient microelectronics to efficient algorithms and hardware, advanced thermal management, and load flexibility to integrate AI technology with a modern electric grid.
• Future Manufacturing – Develop novel approaches to manufacturing that can improve agility and energy and material efficiency, including a predictive understanding of how operations scale from the bench to commercial scale.

For FY2026, this framework has been expanded from the previous description of multi-Area topics to reflect a more complete and enduring framework describing how we advance scientific capabilities. Innovative ideas are sought that can address one or more of the topics below, with an emphasis on how new capabilities span the interest of more than one research area.

Priorities in:

• Novel source and accelerator concepts
• Innovative instrumentation in detection, sensing, measurement, readout and data acquisition
• New means of combining detection and measurement capabilities in networks
• Automation of control and acquisition in scientific facilities/instrumentation (instruments and
  software)
• Novel data management capabilities, including real-time data processing/interpretation
• Development, adaptation, and deployment of models, algorithms, and/or novel ML/AI approaches (integrating multimodal data, digital twins, foundation models, surrogate models accelerating simulation, approaches to uncertainty, etc.)