Six institution-wide Major Initiatives exemplify PNNL’s efforts to integrate scientific discovery and technological innovation to address some of the nation’s most complex and pressing challenges. Each Major Initiative embodies a five- to ten-year commitment of resources to achieve significant, breakthrough progress in its focus area.

Catalysis for Renewable Carbon and Hydrogen (Carbon Catalysis)

This builds on PNNL’s long-standing strengths in chemistry. We intend to demonstrate the ability to store and release energy in chemical bonds reversibly and efficiently, design systems and processes that enable the repeated reuse of carbon atoms, and enable the energy efficient separation and reuse of carbon from a variety of feedstocks, including wastes. These achievements will be necessary if we are to decarbonize the “hard-to-electrify” aspects of our energy systems (e.g., heavy industries such as chemical, steel, and cement production) and meet U.S. net-zero carbon intensity and climate change goals.

Precision Synthesis and Processing to Accelerate Materials Discovery (Precision Materials Synthesis)

The ability to precisely synthesize the mechanical, electronic, and chemical properties of materials to meet performance demands is a central challenge in materials science today. PNNL’s focus is on elucidating the design principles that will enable greater precision with respect to mass, charge, information, and energy transport characteristics during the synthesis and processing of materials at various scales. While both our Carbon Catalysis and Precision Materials Synthesis initiatives emphasize expanding our understanding of fundamental science, we are inspired in each by specific applications, including sustainable transportation fuels, clean hydrogen production, quantum information systems, energy storage technologies, and microelectronics.

Understanding and Predicting Multiscale Earth System Dynamics (Earth System Dynamics)

This initiative will deliver the science necessary to improve the range and confidence of future Earth system models, and thus enhance our ability to plan for, and respond to, climate-driven impacts on U.S. infrastructure, ecosystems, national security, and economic growth. PNNL’s effort will concentrate on areas in which insufficient data, incomplete understanding of key processes and interactions, and/or inadequate modeling tools that do not (yet) fully capture the relevant processes and feedback loops across scales are limiting progress. We are currently focused on atmospheric, land, coastal, and integrated human-Earth systems.

Understanding, Predicting, and Controlling the Microbial Phenome (Predictive Phenomics)

The observable traits of an organism depend on both genomic expression and that organism’s interaction with its environment. To date, the scientific community has relied heavily on inferences from gene sequences and abundance analyses that are often found to be misleading, inaccurate, or incomplete. By contrast, PNNL’s approach is to derive functional information from examination of the relationships between proteins, metabolites, their associated genes, and their environment, specifically with respect to microbes and microbial communities. Our success will allow us to design biological systems that provide new avenues to decarbonization and contribute to the bioeconomy, biosecurity, and human health.

Grid Control and Energy Storage for a Reliable and Resilient U.S. Electric Grid (Grid Control and Energy Storage)

This initiative encompasses linked goals that will allow for the decarbonization, resilience, and security of the U.S. energy system. Our efforts are focused in two areas:

1. grid sensing and control technology that will improve reliability while incorporating dramatically higher levels of electrification and carbon-free generation, and
2. grid-scale, low-cost, long-duration, energy storage materials that will add flexibility and resilience to our existing infrastructure.

Advancing Artificial Intelligence through Algorithmic and Architectural Diversity (AI Algorithms and Architectures)

This initiative is grounded in PNNL’s expertise in data science. It will advance DOE’s ability to apply AI to scientific discovery by developing new architectures and reasoning methods using distributed, edge, and cloud computing across large-scale AI ecosystems; collaborating with industrial AI leaders (e.g., Microsoft and Micron) to codesign specialized computing solutions for important problems in chemistry, materials science, and biology; and expanding our use of AI to drive the automation of experimentation and simulation.