PNNL is leading the next generation of computing for scientific discovery.      
Explore our Computing & AI story

The Computing Division focuses on basic and applied research in computing and mathematics.  Our research programs are focused on intelligent systems, high-performance computing, computational mathematics, and software and data engineering. Our scientists develop and apply novel methodologies for:

• Software Engineering for Data Integration, Visualization, and Analytics
• Multi-Scale Modeling of Complex Systems
• Uncertainty Quantification
• Graph and Data Analytics
• Autonomous Machine Intelligence
• High-Performance Computing
• Domain-Aware and Physics-Informed Machine Learning
• Co-Design to Advance Computing Frameworks

Scientists and engineers in ACMD apply their expertise in mathematics, algorithms, hardware-software co-design and AI to revolutionize scientific discovery, advance computing systems, and accelerate quantum information science.

Software and Data Systems Engineering Group

Group Lead: Angela Norbeck

The Software and Data Systems Engineering group tackles the challenges associated with scientific data management, software and architecture development, and cloud computing. The group designs analytical platforms and user access portals for large and small scale data sources and distributed teams. Our scientists and engineers design and manage data flows at scale, develop web portals to access data, create interactive data visualizations, and leverage hybrid data management architectures to create robust and efficient data solutions.

Teams:

• Data Applications
• Data Systems
• Infrastructure Design
• Scientific Web Interfaces

Computational Mathematics Group

Group Lead: Panos Stinis

Our Computational Mathematics group develops and enables innovative modeling and simulation methodologies that improve the understanding of complex systems. Our core capabilities include multi-scale and multi-fidelity approaches, uncertainty quantification, scientific machine learning and discrete mathematics for forward and inverse problems. In collaboration with academia, the group has pioneered the concept of physics-informed, data-driven methods and their application to problems in challenging domains, such as subsurface transport and ice-sheet modeling. The group also leverages its modeling and simulation capabilities to develop digital twins that will guide the design and operation of next-generation energy systems.

Teams:

• Multiscale Modeling
• Scientific Machine Learning and Uncertainty Quantification

Data Sciences and Machine Intelligence Group

Group Lead: Mahantesh Halappanavar

Our Data Sciences and Machine Intelligence group performs research at the intersection of super intelligence, computer science, data science, and domain science applications. Our capabilities include super intelligence, natural language processing, robotics, data architectures, network science, graph analytics, combinatorial optimization, design and control of complex systems, decision sciences, scientific machine learning, and scalability of methods through high-performance computing. Our scientists work closely with subject matter experts in computational biology, complex infrastructures, high energy physics, computational chemistry, atmospheric sciences, building technologies and the electric power grid, and cyber security, among other domains of national interest.

Teams:

• Autonomous Intelligence: Robotics and autonomy, design and control of complex system, with applications in building technology and power grid, and autonomous science.

• Language Intelligence: Natural language processing, multi-agentic systems, with applications in environmental permitting and monitoring, and basic energy sciences.

• Decision Intelligence: Combinatorial optimization, decision sciences, and deep reinforcement learning, with applications in critical infrastructure networks, electronic design automation, cybersecurity and defense technologies. 

Future Computing Technologies Group

Group Lead: Kevin Barker

Our Future Computing Technologies group conducts fundamental computer science research targeting future generations of scientific computing technology, including hardware design automation tools; architectural assessment, evaluation, and testing; emerging and disruptive computing paradigms; hardware/software codesign; and SI-enabled scientific workflows spanning the computing continuum. Our research looks beyond the High-Performance Computing facility to imagine a world in which a seamless computing ecosystem enables agile and rapid scientific discovery.  The group’s deep expertise in architecture design and testing flows, digital and analog computing systems, large-scale and edge computing, system software, and performance modeling and analysis lay a strong foundation for shaping the future of scientific computing and the next generation of data science and computational capabilities for PNNL.

Teams:

• Continuum Computing
• Innovative Emerging Computing
• NextGen Architecture Design

Computer Analytics and Modeling Group

Group Lead: Satish Karra

Our Computing, Analytics, and Modeling (CAM) group develops and integrates computational methods to advance predictive understanding of biological and environmental systems. Our capabilities include computational biology, statistical analysis, data integration and visualization, molecular simulation, metabolic modeling, hydrology and reactive transport, and scientific machine learning. Our scientists collaborate closely with experimental researchers to transform complex data into scientific insights and connect models with experiments across scales, from molecules to watersheds. The group advances autonomous science through intelligent agents, automated analysis and modeling workflows, and integrated computational infrastructure that connect experimental planning, data interpretation, and predictive modeling. These capabilities accelerate discovery in biodesign, critical materials recovery, and the cycling of nutrients and contaminants in the environment.

Teams:

• Data Transformations

• Systems Modeling