Berkeley Lab Computing Sciences

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Berkeley Lab Computing Sciences

Berkeley Lab Computing Sciences area operates two Dept. The Computing Sciences organization was created to advance computational science throughout the U.S.

of Energy national user facilities — NERSC & ESnet — as well as conducts research in computer science, computational science and applied math to achieve transformational breakthroughs in science. Berkeley Lab's Computing Sciences organization researches, develops, and deploys new tools and technologies to advance research in such areas as global climate change, combustion, fusion energy, nanotechn

ology, biology, and astrophysics. Department of Energy's Office of Science research programs. The organization includes:

The Computational Research Division (CRD)

CRD creates computational tools and techniques that enable scientific breakthroughs by conducting applied research and development in computer science, computational science, and applied mathematics. http://crd.lbl.gov/

The National Energy Research Scientific Research Computing (NERSC) Center

NERSC is home to some of the world’s most efficient supercomputers. This center is a leader in providing systems, services and expertise to advance computational science throughout the Department of Energy research community. http://www.nersc.gov/

The Energy Sciences Network (ESnet)

ESnet provides high-bandwidth, reliable connections to researchers at national laboratories, universities and other institutions, across the United States. These world-class connections provide the collaborative capabilities needed to address some of the world’s most important scientific challenges. http://www.es.net/

07/16/2026

Accelerating scientific discovery with AI starts with the right infrastructure. And in an insightful Q&A, Energy Sciences Network (ESnet) Director and American Science Cloud Project Deputy Inder Monga explains how this initiative is unifying data and HPC to power the U.S. Department of Energy's Genesis Mission.
Story link in the comments ⬇️

Advanced Light Source Berkeley Lab

07/02/2026

Happy 250th, America! 🇺🇸 🎆
As the nation approaches its semiquincentennial, we’re proud to reflect on Berkeley Lab Computing Sciences’ legacy of U.S. innovation. For nearly eight decades, our researchers at Berkeley Lab, NERSC, and Energy Sciences Network (ESnet) have pioneered the foundational math, supercomputing, and advanced networks that keep the U.S. at the global forefront of discovery—from exascale architectures to autonomous, AI-driven labs, and quantum computers. To mark this historic milestone, we even contributed an 8-qubit quantum chip to the national time capsule. ⚛️
Take a journey through the breakthroughs that continue to drive American innovation. 💻 📡
Explore our historical timeline below. ⬇️

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cc: University of California

06/30/2026

Did you know that an 8-qubit Quantum Processing Unit (QPU) from Berkeley Lab’s Advanced Quantum Testbed (AQT) is headed for the official America250 time capsule? 🇺🇸
Designed in collaboration between UC Berkeley and Berkeley Lab, this groundbreaking chip is the first of its kind to be distributed globally. It has empowered researchers worldwide to test new quantum computations and simulate complex phenomena, from neutrino interactions to the internal structure of nuclei. ⚛️
The capsule will be buried this July 4th in Philadelphia and opened 250 years from now for the nation’s 500th birthday. Congratulations to Kan-Heng Lee, Irfan Siddiqi, and the entire Berkeley Quantum ecosystem for giving future generations a tangible glimpse into the dawn of the quantum era!
Read more below ⬇️

cc: Governor Gavin Newsom University of California

06/29/2026

To boost agricultural resiliency and engineer next-generation bioenergy crops, researchers need to understand how plants and microbes interact. But this is notoriously difficult to study because subtle differences in materials, methods, or even the hands of the researchers themselves can lead to inconsistent results.
Leveraging our tradition of team science, Berkeley Lab biologists, mathematicians, and engineers built EcoBOT. More than just an automated observation tool, this “self-driving” lab uses advanced computer vision to track plant growth with unprecedented precision. It then applies mathematical algorithms, like Gaussian processes, to analyze that data and autonomously design and perform follow-up experiments.
By eliminating human variability, EcoBOT ensures complex biology can be reliably reproduced anywhere, accelerating the path to a secure agricultural and energy future.
Learn more below ⬇️

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06/25/2026

Chiral 2D metal halide perovskites have massive potential for future technologies like advanced LEDs and spin-based electronics, but there is a catch: they are notoriously difficult to produce consistently. Even when following the exact same recipe, material performance can vary wildly from one lab to the next.
To address this reproducibility crisis, a collaboration—including Maher Alghalayini and Marcus Michael Noack from Berkeley Lab’s Applied Mathematics and Computational Research (AMCR) division—developed a powerful data-driven framework. By combining experimental data with advanced statistical modeling and machine learning, they decoded the complex, hidden relationships between how these materials are made and how well they perform.
Rather than guessing, their tools identified the exact synthesis “knobs" needed for success. The data revealed that liquid solvent choice is the single biggest driver of consistency. By using a specific solvent and precisely tuning baking temperature and film thickness, they found a reliable way to maximize the material’s ability to interact with circularly polarized light.
Read more below. ⬇️

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U.S. Department of Energy Molecular Foundry Advanced Light Source

06/15/2026

Last week, Berkeley Lab's Computing Sciences Area welcomed 138 students for the summer! 🎉
For the next 12 weeks, they will be diving deep into the world of scientific discovery, working directly alongside our researchers to gain hands-on experience in HPC, data science, networking, applied mathematics, and so much more. It all builds up to a massive poster session on August 17, where the students get to show off their research. 💻✨
We can’t wait to see what they accomplish. 🙌
Check out this year’s full schedule below 👇

06/01/2026

Quantum computers hold the key to discovering new materials, but today’s hardware struggles to calculate “excited” molecular energy states. To overcome this, Berkeley Lab and Harvard University researchers developed a breakthrough hybrid framework called multiobservable dynamic mode decomposition (MODMD).
Instead of running long operations that easily overwhelm near-term quantum hardware, MODMD takes quick quantum “snapshots” of a system over time. A classical computer then pieces this data together to predict multiple energy levels accurately.
By offloading the heavy analysis to classical algorithms, this method extracts vital molecular data using a fraction of the usual computing power—a major step toward practical quantum chemistry! ⚛️
Learn more: https://bit.ly/MODMD

cc: U.S. Department of Energy NERSC

05/27/2026

How will the semiconductor industry power tomorrow’s AI systems without overwhelming global power grids?
In a recently published Nature Reviews Electrical Engineering paper, Berkeley Lab's John Shalf joins a global team of industry, academic, and national lab researchers to examine the physical limits of current chip technology. The paper proposes that updated industry roadmaps for heterogeneous integration are needed to coordinate advances in chip packaging, cooling, and power delivery.
Learn more: https://bit.ly/HIroadmap

cc: U.S. Department of Energy

05/18/2026

Popular AI tools (LLMs) can write code and draft emails, but they have a massive blind spot: they cannot “see” the 3D physical world. 💻
To deliver on the promise of AI for science, Berkeley Lab researchers created MatterChat. Published in Nature Machine Intelligence, this new framework serves as a specialized bridge, providing conversational AI with the “structural vision” it needs to understand complex atomic structures. ⚛️🔬
By seamlessly connecting standard text models with specialized physics models, MatterChat turns commercial AI into a powerful partner for discovering next-generation materials. 🧠
Read the story: https://bit.ly/MatterChat

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cc: NERSC

03/31/2026

A huge congratulations to Berkeley Lab's Lin Lin for being named a 2026 Society for Industrial and Applied Mathematics (SIAM) Fellow! 🎉 🎉 🎉
This honor recognizes his contributions to numerical analysis, new methods, and software for solving electronic structure problems in computational chemistry and materials sciences.

cc: UC Berkeley

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