Skip to navigation Skip to content
Careers | Phone Book | A - Z Index


Computational Research at Berkeley Lab

Meet some of our scientists and learn more about Computational Research at Berkeley Lab in this short video.


Porous materials, such as zeolites and metal-organic frameworks (MOFs), are growing more important as materials for energy-related applications, including carbon capture and gas storage. Computational researchers at LBNL have developed tools that analyze porous materials and predict if a molecule can traverse a material's voids, map accessible parts of these void spaces and calculate accessible volumes and surfaces. (Scientific research by Maciej Haranczyk and James Sethian. Visualization by Richard Martin and Prabhat.)

The Computational Research Division conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture and high-performance software implementation. We collaborate directly with scientists across LBNL, the Department of Energy and industry to solve some of the world’s most challenging computational and data management and analysis problems in a broad range of scientific and engineering fields, including materials science, biology, climate modeling, astrophysics, fusion science, and many others. We also develop advanced capabilities for scientific data understanding through visualization and analytics. We perform research in high-performance computing (HPC) technology for extreme-scale computing systems, including research into performance optimization, performance analysis, benchmarking, and performance engineering of scientific applications, compilers, operating systems, and runtime systems. We also perform research in cloud computing, workflow tools, scheduling, computational frameworks and distributed systems.  Our products range from peer-reviewed scientific publications to scientific research codes to end-to-end computational and data analysis capabilities that enable scientists and engineers to address complex and large-scale technical challenges.